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    Reverse Engineering of Automotive PartsApplying Laser Scanning and Structured

    Light Techniques

    Project in Lieu of Thesis

    presented for the

    Masters of Science

    Degree

    The University of Tennessee, Knoxville

    Ngozi Sherry Ali

    May 2005

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    ACKNOWLEDGEMENTS

    I would like to first give thanks to Dr. M. Abidi for allowing me the opportunity to join

    the IRIS lab as a graduate research assistant. His support and patients has allowed me to

    complete one of my many life goals. Thanks for giving me the chance to earn a degree ofhigher education and to reach heights that I knew I could reach. Saying thank you will

    never be enough to repay you for all you have done. Again, I thank you for everything

    and I am very appreciative.

    I would like to thank Dr. Koschan for advising me on all my work and projects. I value

    the guidance that was giving to me. Thanks for being the best advisor a student could askfor. I would also like to thank Dr. Page for always being there when I needed. There was

    never a time when I was in need that you did not offer you assistance.

    The IRIS Lab has been my family since I first arrived in August of 2003, I thank all thatis apart of the Lab. In closing, I would like to thank Vicki Courtney-Smith and Kim Cate

    for always making sure I had all the little things that I couldnt get myself. Special

    thanks go to Rangan and Chris Kammerud for helping me with my research and classwork. Thanks to my close friends Sharon Sparks and Syreeta Dickerson for helping me

    through my first year of school with the support and encouragement that they gave me.

    Lastly, thanks to my family for always supporting me and my endeavors. You all are the

    driving force in my life and career. Without your love, none of this would matter.

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    i

    ABSTRACT

    The automotive industry has an increasing need for the remanufacturing of spare parts

    through reverse engineering. In this project we will review the techniques of laser

    scanning and structured lighting for the reverse engineering of small automotive parts.

    Laser Range Scanning is the use of a CCD camera that captures the profile of the laser asit passes on an object. Structured light is the projection of a light pattern on an object also

    with the use of several cameras to obtain a profile of the object. The objective of the

    project is to be able to generate part-to-CAD and CAD-to-part reconstruction of theoriginal for future usage. These newly created 3D models will be added to the IRIS 3D

    Part Database.

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    ii

    TABLE OF CONTENTS

    1 INTRODUCTION..................................................................................................... 1

    2 ACQUISITION CLASSIFICATIONS AND RELATED WORKS ..................... 5

    2.1 Contact Data Acquisition Techniques ................................................................ 6

    2.2 Reverse Engineering Applying Contact Techniques .......................................... 7

    2.3 Non-Contact Data Acquisition Techniques ........................................................ 8

    2.4 Reverse Engineering Applying Non-Contact and Hybrid Techniques............. 10

    2.5 General Constraints of Data Acquisition Techniques....................................... 12

    3 SURFACE RECONSTRUCTION ........................................................................ 15

    3.1 Single Views and Data Segmentation............................................................... 15

    3.2 Multiple View Integration and Registration ..................................................... 17

    3.3 Post-processing Registered Images................................................................... 19

    4. SYSTEM DESCRIPTIONS AND SETUP ........................................................... 21

    4.1 IVP Range Scanning Profiling System............................................................. 21

    4.2 Genex 3D FaceCam Profiling System.............................................................. 25

    5 RESULTS AND DISCUSSIONS........................................................................... 285.1 Part Identification.............................................................................................. 28

    5.1 IVP Laser Range Data Results.......................................................................... 31

    5.2 Genex Structured Light Data Results ............................................................... 36

    5.3 Comparison: IVP Laser Range Scanner and Genex 3D FaceCam Data.......... 45

    6 CONCLUSION ....................................................................................................... 49

    REFERENCES................................................................................................................ 51

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    iii

    LIST OF FIGURES

    Figure 1.1: Flowchart for basic transformation phases of reverse engineering................. 2

    Figure 1.2: The sequence of steps required for the reconstruction of a model from

    multiple overlapping scans. ........................................................................................ 4

    Figure 2.1: Classification of data acquisition techniques used in contact and non-contactapproaches for reverse engineering systems............................................................... 5

    Figure 3.1: (a) Original image of the water pump, (b) Sequenced single view range

    images of the bottom surface of the water pump generated using our laser rangescanner ...................................................................................................................... 16

    Figure 3.2: Textured single view range images generated from the structured lighting

    system, (a) left view of the ramp, (b) front view of the ramp, (c) back/left view ofthe ramp, (d) front/ right view of the ramp. .............................................................. 17

    Figure 3.3: Point cloud information of the side view of the water pump, (a) and (b) side

    view range images, (c) registered range image of the two side views...................... 19Figure 4.1: Principle of a laser triangulation system. Arrangement for the IVP Smart

    Vision Camera and Laser.......................................................................................... 22

    Figure 4.2: Equipment setup for IVP Range Scanning System, (a) front view of the IVP

    Range Scanner, (b) side angled view of the top of the IVP Range Scanner............. 23

    Figure 4.3: Calibration grid for the IVP Range Scanner ................................................. 24

    Figure 4.4: IVP Range Scanner User Interface................................................................ 24

    Figure 4.5: Structured light grid pattern projected on the ramp with a neutral tanbackground................................................................................................................ 25

    Figure 4.6: Genex 3D FaceCam System.......................................................................... 26

    Figure 4.7: Distance specification for data collection ..................................................... 27

    Figure 4.8: Genex 3D FaceCam User Interface screen that displays the left, center andright camera photos of the object.............................................................................. 27

    Figure 5.1: Photos of the ramp part (a) front and side views, (b) back and top views, (c)

    ramp measurements .................................................................................................. 29

    Figure 5.2: Photos of the water pump part (a) top and side views (b) bottom view........ 30

    Figure 5.3: Photos of the pulley arm, (a) is the top view (b) is the bottom view ............ 30

    Figure 5.4: CAD model images of the ramp (a) left/front view, (b) right side view, (c)bottom view, (d) back view ...................................................................................... 31

    Figure 5.5: 3D Ramp CAD models, (a) left/front side view, (b) back/left side view, (c)

    bottom view, (d) right/top/ front side view............................................................... 32

    Figure 5.6: Reconstructed bottom surface of the water pump, (a) Point cloud

    information, (b) Solid mesh model, (c) smooth reconstructed mesh model............. 33Figure 5.7: Reconstructed views of the water pump, (a) reconstructed top view (b) side

    view reconstruction................................................................................................... 34

    Figure 5.8: Cleaned point cloud data of the side views of the water pump taken with the

    IVP Range Scanner, (a) right view, (b) left view, (c) right view and (d) left view.. 35

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    v

    LIST OF TABLES

    Table 1..47

    Table 2..48

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    Chapter 1: Introduction 1

    1 INTRODUCTION

    Engineering is a growing field that continues to evolve to suit the rapid changes of the21st century. Engineering fields are constantly improving upon current designs and

    methods to make life simple and easier. When referring to technology, simple and easy

    can be directly related to fast and accurate. Simple meaning that, you do not use upvaluable time in assembly or doing a specific task. Easy meaning how many times you

    will have to do the process or task.

    When we think of engineering we think of the general meaning of designing a productfrom a blue print or plan. Engineering [36] is described as the application of scientific

    and mathematical principles to practical ends such as the design, manufacture, and

    operation of efficient and economical structures, machines, processes, and systems. Thistype of engineering is more commonly known as Forward Engineering. An emerging

    engineering concept is utilizing forward engineering in a reverse way. This method is

    more commonly referred to as Reverse Engineering.

    Reverse engineering is the opposite of forward engineering. It takes an existing product,

    and creates a CAD model, for modification or reproduction to the design aspect of the

    product. It can also be defined as the process or duplicating an existing component bycapturing the components physical dimensions. Reverse engineering is usually

    undertaken in order to redesign the system for better maintainability or to produce a copy

    of a system without access to the design from which it was originally produced.

    With this knowledge, computer vision applications have been tailor to compete in the

    area of reverse engineering. Computer vision is a computer process concerned withartificial intelligence and image processing of real world images. Typically, computer

    vision requires a combination of low-level image processing to enhance the image quality

    (e.g. remove noise, increase contrast) and higher level pattern recognition and imageunderstanding to recognize features present in the image. Three-dimensional (3D)

    computer vision uses two-dimensional (2D), images to generate a 3D model of a scene orobject.

    There has been a mandatory need for 3D reconstruction of scenes and objects by the

    manufacturing industry, medical industry, military branches and research facilities.

    Manufacturing industry utilizes reverse engineering for its fast rapid prototyping abilitiesand accuracy associated with the production of new parts. This fast prototyping is done

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    Chapter 1: Introduction 2

    through the use of CAD model designs for inspection purposes. Military branches also

    utilize reverse engineering to perform inspection task that are associated with safety.

    One of our laboratorys current focuses is reverse engineering or 3D reconstruction of

    objects and scenes from real world data. The goal of reverse engineering an object is tosuccessfully generate a 3D CAD model of an object that can be used for future modeling

    of parts where there exists no CAD model. We want to generate clean, smooth 3D

    models, which are free of noise and holes. This requires a strong, robust imageacquisition system that can acquire data with a high level of accuracy in a sufficient time

    frame. Our system uses range and intensity images of objects as input. The output is

    transformed data that is represented as 3D reconstructions of geometric primitives.

    There are several building blocks or steps, which determine the process of building a

    complete 3D model from range and intensity data. These steps, listed in Figure 1.1 show

    the format of how range image data is acquired, transformed and generated. This

    flowchart can be characterized as a generic basic principle for reverse engineering. Thesteps shown often overlap during the process of each stage.

    Figure 1.1: Flowchart for basic transformation phases of

    reverse engineering

    There are many different approaches to acquiring 3D data of objects of various structuralshapes. All 3D-based machine vision systems ultimately acquire and operate on image

    data. Acquisition can be based on collecting the Z-axis data using linear area, laser radar

    laser scanning techniques, point detectors, or other approaches. These systemsincorporate the computer power to manage, process and analyze the data acquired. In

    addition to these tasks, make decisions relating the data to the application without

    1. Data Capture

    2. Data

    Segmentation

    3. 3D CAD

    Model

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    Chapter 1: Introduction 3

    operator intervention. This characterizes what is meant by the term 3D-based machine

    vision.

    Traditional processes for reverse engineering of objects and structures from 3D datasets

    have been initial data (e.g. triangulated models) and parametric surface (e.g. quadraticsurface) driven. These approaches have been successful for simple parts, but have

    resulted in reconstructions that have errors when dealing with more complex structures.

    Typical errors arise from noisy data or missing data from the surface of the part. Othererrors can also consist of incorrect relative positions of the object. Industries are looking

    for a method to improve upon these errors and migrated toward a fast efficient way of

    modeling parts for inspection purposes. Traditional practices use CMMs, which arecoordinate measuring machines that have a touch probe to model the surface for

    inspection. Todays industries are moving toward the improvement of better accuracy and

    faster inspection time. This can be improved through the integration of laser range

    scanning.

    When implementing a non-contact measurement solution, the end user has a large array

    of commercial systems to select from. To emphasis the use of this project we have chosentwo different commercial systems with two different approaches to modeling 3D objects

    using vision based technology. Our approaches will be using data acquisition systems

    that are fairly robust to noise and yield high accuracy measurements. The two differenttechniques consist of using laser lighting and structured lighting techniques. The first

    system is the IVP Range Laser Scanning System and the second is the Genex 3D

    FaceCam System.

    Although both systems primary focus is reconstruction of real world objects and scene,

    we will investigate the limitation of both systems. Although the structured lightingsystem is not designed for reverse engineering use, we will compare the modeling aspects

    of this system for reverse engineering of automotive parts to the laser range system.

    Figure 1.2 describes the data flow of our approach applied to both systems. While theapproaches are similar, the steps taken may vary for each system.

    Figure 1.2 is a more detailed description of Figure 1.1. The blue area best describes thedata capturing section, while the yellow and orange highlights the data pre and post-

    processing steps and the final outcome is a 3D CAD model. In the data segmentation

    stage several steps are taken to generate noise free, smooth models of the part. In datareduction, data such as the noise, outlier or erroneous background information is

    eliminated. Outliers are false data points that are captured during acquisition. Surfacesmoothing and multi-view registration are included in data integration. Surface

    smoothing is an additional feature to eliminate noisy data and make the surface of theobject more uniform in texture. This can be performed both before and/or after several

    views of the part are merged. After all the steps are complete a final 3D CAD model is

    generated. Data segmentation and integration are discussed in depth in section 3.

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    Chapter 1: Introduction 4

    Figure 1.2: The sequence of steps required for the reconstruction of a model from

    multiple overlapping scans.

    Section 2 of this paper, will discuss the related merits and methods of reverse engineering

    and techniques. Section 3 will discuss surface reconstruction for 3D models. Following,is section 4 that discusses the applications and technique of our implemented systems.

    Finally, we will conclude this paper with the implementation of our procedure for the

    experimental results. A comparison and summary of the limitations will be expressed inthe conclusion section of the paper.

    3. Data Integration

    1. Data Capture

    2. Data Segmentation

    Pre-processing

    Data Reduction

    Next Best View

    Plan

    Noise Filtering

    Acquisition of

    Range Images

    Post-processing

    Multi-view

    Registration

    Hole Filling

    Surface

    Smoothing

    4. 3D CADModel

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    Chapter 2: Acquisition Classifications and Related Works 5

    2 ACQUISITION CLASSIFICATIONS AND

    RELATED WORKS

    An important part to reverse engineering is data acquisition. Data acquisition systems are

    constrained by physical considerations to acquire data from a limited region of an

    objects surface. Therefore, multiple scans of the surface must be taken to completely

    measure a part. After reviewing the most important measuring techniques, the related

    merits and difficulties associated with these methods are discussed. Figure 2.1 classifiesthe types of application used for acquiring 3D data into contact and non-contact methods.

    Figure 2.1: Classification of data acquisition techniques used in contact and non-contact approaches for reverse engineering systems.

    Data Acquisition Methods

    Non-contact Methods Tactile Contact Methods

    Magnetic Acoustic Optical

    Laser

    Triangulation

    CMMs Robotic Arms

    Stereo Analysis

    Time-of-FlightStructured Lighting

    Interferometers

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    Chapter 2: Acquisition Classifications and Related Works 6

    2.1 Contact Data Acquisition Techniques

    There are many different methods for acquiring shape data, as shown in Figure 2.1.

    Tactile methods represent a popular approach to shape capture. The two most commonlyknown forms are Coordinate Measuring Machines (CMMs) and mechanical or robotic

    arms with a touch probe sensing device. CMMs are often used when high precision isrequired. It is considered a contact type method that is NC-driven and can program

    sampling of points for predefined features efficiently [16]. These machines can be

    programmed to follow paths along a surface and collect very accurate, nearly noise-freedata. Sampling basis is the only way a part can be inspected using a CMM. The ability

    for obtaining large amounts of point data from the parts surface quickly for complete

    inspection needs to be the number one quality of an inspection device. This needs to be

    done in conjunction with the idea that the part has free-forming surfaces. A 3-axis millingmachine is an example of a mechanical or robotic arm. These machines can be fitted with

    a touch probe, as mentioned before, and used as a tactile measuring system. However, itis not very effective for concave surfaces. There are many different other robotic deviceswhich are used because of their ability to have less noise and have a desirable accuracy,

    but like the CMM, they are the slowest method for data acquisition.

    There are disadvantages when using a CMM or robotic arm to model surfaces of parts.

    The disadvantages of CMMs having contact to the surface of an object can damage the

    object. The reason being is if the surface texture is soft, holes can be inflicted on the

    surface. CMMs also show difficulties in measuring parts with free form surfaces. Thepart might have indentions that are too small. Flexibility of parts makes it very difficult to

    contact the surface with a touch probe without creating an indentation that detracts from

    the accuracy of the measurements. For CMM, geometric complexity increases thenumber of points required for accurate measurements. The time needed to capture points

    one by one can range from days or sometimes weeks for complicated parts. There arealso external factors that affect the accuracy of a CMM. The main ones are temperature,

    vibration and humidity [1].

    Xiong [33] gives an in depth discussion of measurement and profile error in tactile

    measurement. Sahoo and Menq [26] use tactile systems for sensing complex sculptured

    surfaces. Butler [4] provides a comparison of tactile methods and their performance.

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    Chapter 2: Acquisition Classifications and Related Works 7

    2.2 Reverse Engineering Applying Contact Techniques

    Reverse engineering is a growing industrial market for manufacturing and development.

    Various individuals and groups have developed new techniques, which have beenimprovements to the current existing techniques available.

    The first technique and method we visit is that of Thompson at el., [28]. Their research

    describes a prototype of a reverse engineering system which uses manufacturing features

    as geometric primitives for mechanical parts. Their method is geared toward reverseengineering of mechanical parts. They have identified a feature based approach that they

    state produces highly accurate models, even when the original 3D sensor data has

    substantial errors. In the feature based approach, the registration of two different point

    clouds is performed by matching three points to three points, three spheres to threespheres, or three planes to three planes [28].

    Their main innovation was to use the features to fit scanned data, rather than usingtriangulated meshes or parametric surfaces patches. This research claims to have

    advantages over the current practice of ordinary CMMs. It states the resulting models can

    be directly imported into featured-based CAD systems without loss of the semantics andtopological information inherent in featured-based representations. It assumes that the

    models are already generated into CAD model formation.

    The results from Thompson et al., [28] for quantitatively evaluating the accuracy of themodels using the feature-based modeling approach, parts were used only if they had the

    original CAD model. The part was machined out of aluminum using a 3-axis NC mill. A

    non-contact digitizer measured the surface points. New CAD models were generatedusing the REFAB (Reverse Engineering FeAture-Based) system. The system designs a

    model composed of mechanical features from a set of 3D surface points that could bedefined by users. The geometric differences between the original and new generated

    model was computed. Further details and results from their work can be read in reference

    [28].

    The second technique we visit is that of Yu Zhang [35]. This research focuses on the

    engineering application of reverse engineering. The system employed is built with acoordinate measurement machine and CAD/CAM software. By scanning the physical

    object, the measurement data is acquired. The basic principles of reverse engineering was

    applied to the design and manufacturing of the die of a diesel engine. The process

    described in the paper is the object digitization and CAD model reconstruction to NCmachining.

    The dies geometric shape is measured and data is acquired using a CMM in conjunctionwith KUM measurement software that has a linear scan mode. The number of points

    measured is determined automatically by the CMM according to the curvature change of

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    Chapter 2: Acquisition Classifications and Related Works 8

    the surface. This is measured on the tactile point. The CMM that is used can measure

    about 1600 points for each scanned curve.

    Usually a machining tracing process results in a structured point of sequences with a

    large number of points and a line structure. The result of Zhangs [35] system is a self-developed program to realize the transformation of the format of the measured data

    information from the CMM and KUM. First, the format of the measured data is

    transformed into an acceptable format for the software used. Then the data is filtered outand processed in a visualized way. The processed data is directly used for the creation of

    the die CAD model. After the CAD model of the die is complete, the NC machining

    process planning can generate the location for cutting the manufacturing application. Thedie is finally machined by the NC machine tool using the created CAD model.

    2.3 Non-Contact Data Acquisition TechniquesNon-contact methods use light, sound or magnetic fields to acquire shape from objects. In

    the case of contact and non-contact, an appropriate analysis must be performed todetermine the positions of the points on the objects surface. Each method has strengths

    and weaknesses that require the data acquisition system to be carefully selected for the

    shape capture functionality desired.

    Optical methods of shape capture are probably the broadest and growing in popularity

    over contact methods. This is because they have relatively fast acquisition rates. There

    are five important categories of optical methods: laser triangulation, time-of-flight,

    interferometers, structured lighting and stereo analysis. This section will discuss thevarious principles of each method.

    Laser Triangulation is a method, which uses location and angles between light sources

    and photo sensing devices to deduce position. A high-energy light source is focused and

    projected at a pre-specified angle at the surface of interest. A photosensitive device,usually a video camera, senses the reflection of the surface and then by using geometric

    triangulation from the known angle and distances, the position of a surface point relative

    to a reference plane can be calculated. The light source and the camera can be mounted

    on a traveling platform which then produces multiple scans of the surface. These scansare therefore relative measurements of the surface of interest. Various different high-

    energy light sources are used, but lasers are the most common.

    Triangulation can acquire data at very fast rates. The accuracy is determined by the

    resolution of the photosensitive device and the distance between the surface and the

    scanner. Motavalli et al., [19] presents a reverse engineering strategy using lasertriangulation. Moss et al., [22] present a detailed discussion of a classic laser triangulation

    system used to capture shape data from facial surfaces. The use of laser triangulation on a

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    Chapter 2: Acquisition Classifications and Related Works 9

    coordinate measuring machine is presented by Modjarred [20]. These references give a

    broad survey of methods, approaches and limitations of triangulation.

    Measuring distance by sensing time-of-flight of the light beams emitted is the way a

    ranging system works. Practical methods are usually based on lasers and pulsatingbeams. For example, in laser range finders, the time-of-flight is used to determine the

    distance traveled, and in stereo analysis the relative locations of landmarks in multiple

    images are related to position. Interferometermethods measure the distance in terms ofwavelengths using interference patterns. This can be a very accurate method of

    measurement since visible light has a wavelength of the order of hundreds of nanometers,

    while most reverse engineering applications distances are in the centimeter to meterrange. In principle, other parts of the electromagnetic spectrum could also be used. In

    practice, a high-energy light source is used to provide both a beam of monochromatic

    light to probe the object and a reference beam for comparison with the reflected light.

    Moring et al., [21] describe a range finder based on time-of-flight calculations. The

    article presents some information on accuracy and performance. Jarvis [14] presents anin-depth article on time-of- flight range finders giving detailed results and analysis.

    Structured lighting involves projecting patterns of light upon a surface of interest and

    capturing an image of the resulting pattern as reflected by the surface. The image must

    then be analyzed to determine coordinates of data points on the surface. A popularmethod of structured lighting is shadow Moire, where an interference pattern is projected

    onto a surface producing lighted contour lines. These contour lines are captured in an

    image and are analyzed to determine distances between the lines. This distance isproportional to the height of the surface at the point of interest and so the coordinates of

    surface points can be deduced. Structured lighting can acquire large amounts of data with

    a single image frame, but the analysis to determine positions of data can be rathercomplex. Will and Pennington [30] use grids projected onto the surface of objects to

    determine point locations. Wang and Aggarwal [32] use a similar approach but use

    stripes of light and multiple images.

    The final optical shape capture method of interest is stereoimage analysis. This is similar

    to structured lighting methods in that frames are analyzed to determine coordinate data.However, the analysis does not rely on projected patterns. Instead, typically, stereo pairs

    are used to provide enough information to determine height and coordinate position. This

    method is often referred to as a passive method since no structured lighting is used.Active methods are distinguished from passive methods in that artificial light is used in

    the acquisition of data. Correlation of image pairs and landmarks within the images arebig difficulties with this method and this is why active methods are preferred. Another

    stereo image analysis approach deals with lighting models, where an image is comparedto a 3D model. The model is modified until the shaded images match the real images of

    the object of interest. Finally, intensity patterns within images can be used to determine

    coordinate information.

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    Chapter 2: Acquisition Classifications and Related Works 10

    The final types of data acquisition methods we will examine are acoustic, where sound is

    reflected from a surface, magnetic, where a magnetic field touches the surface and ahybrid of both contact and non-contact. Acoustic methods have been used for decades for

    distance measuring. Sonar is used extensively for this purpose. Automatic focus cameras

    often use acoustic methods to determine range. The method is essentially the same astime-of-flight, where a sound source is reflected off a surface and then distance between

    the source and surface is determined knowing the speed of sound. Acoustic interference

    or noise is often a problem as well as determining focused point locations. Dynamicimaging is used extensively in ultra-sound devices where a transducer can sweep a cross-

    section through an object to capture material data internal to an object.

    Magnetic field measurement involves sensing the strength of a magnetic field source.

    Magnetic touch probes are used which usually sense the location and orientation of a

    stylus within the field. A trigger allows the user to only record specific point data once

    the stylus is positioned at a point of interest. Magnetic resonance is used in similar

    applications to ultra-sound when internal material properties are to be measured. MRI(magnetic resonance) activates atoms in the material to be measured and then measures

    the response.

    Hybrid modeling systems are a combination of contact and non-contact systems. They

    can also be a combination of NC coding and laser scanning techniques. The first typeusually consists of the coordinate measuring machine and integrated laser based

    technology. The second may consist of some other form of a non-contact technique such

    as software and laser-based technology integrated as one system. Hybrid basedapplications will be discussed in the next section.

    To conclude this section, all measuring methods must interact with the surface or internalmaterial using some phenomenon, either light, sound, magnetism or physical surface

    contact. The speed with which the phenomenon operates as well as the speed of the

    sensor device determines the speed of the data acquisition. The sensor type selected alsodetermines the amount of analysis needed to compute the measured data and the

    accuracy.

    2.4 Reverse Engineering Applying Non-Contact and Hybrid

    Techniques

    The first non-contact techniques that we explore are that of Fan and Tsai [13]. Theypresent a measurement system that includes the combination of two CCD cameras, a line

    laser and a three-axis motion stage. They formed an optical non-contact scanning setup

    that works with the mathematical method of direct shape error analysis for engineeringpurposes. The profile measurement of free-form objects can be analyzed. Matching the

    images of the free-form surfaces with sufficient efficiency and accuracy is the final result.

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    Chapter 2: Acquisition Classifications and Related Works 11

    Fan and Tsai research adopted the bicubic uniform B-spline interpolation approach for

    the shape error analysis method. This method was used to describe the first set ofmeasurement point and to generate reconstructed multiple patches of the surface. The

    shape error analysis main function is to sum the squared nearest distances [13]. Based on

    this principle they have developed an algorithm called the direct method or DSEAM(Direct shape error analysis method). This algorithm is an adopted variation of the shape

    error algorithm, and the DFPM (Davidon-Fletcher-Powell Method) algorithm. Refer to

    Fan and Tsai [13] for detailed information on the DFPM algorithm. The B-spline surfaceconstruction and the DFPM algorithm are the foundation of their algorithm. They

    developed a computer program to analysis the shape error with respect to the surface that

    is referenced.

    The results of their approach are of that used on a free-form surface and a car rear-view

    mirror case. They report the rigid body transformation from the optimal shape error

    results and the optimal parameters using the DSEAM. They have reported a reduction in

    the shape error from their technique compared to the initial shape error of the objects.Further reading on their results can be viewed in reference [13].

    The first hybrid-based technique reviewed is that explored by Jim Clark [8]. The

    technique focuses on modeling complex and free-form shapes of mechanical objects by

    comparing contact and non-contact methods for digitizing the surface. A hybrid-triangulation based hand held system integrated with a coordinate measuring machine is

    used for this approach.

    The effects of ambient lighting are discussed for non-contact systems. Whether or not

    the system can measure the ambient lighting depends on the projected color of light on

    the object. Clark summaries by writing that if a system projects laser light then theunwanted frequencies can be filtered out. If the system projects white light, then no

    particular frequencies can be blocked out. This is because it might be carrying the

    information required to measure the object. Therefore, white light area based systems willbe limited in their ability to measure ambient lighting verses laser based systems.

    The results from Clark are of that modeled using a water pump. The water pump wasscanned using both a contact and non-contact system. The results were compared based

    on the surface quality and the point cloud data obtained. He demonstrates that non-

    contact techniques in conjunction with advanced surfacing and inspection software yieldsufficient results for the mechanical design process. Further reading of his work can be

    viewed in reference [8].

    Chow et al., [7] developed an integrated laser-based reverse engineering and CAMmachining system called RECSI (Reverse Engineering and CAM System Integration).

    They evaluate the feasibility of using concurrent engineering and reverse engineering

    methods with the data from laser scanning to remanufacture complex geometrical parts.

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    Chapter 2: Acquisition Classifications and Related Works 12

    The first phase of their research demonstrates that laser scanning and CAD model

    reconstruction can duplicate aircraft structural components accurately and efficientlywithin a given tolerance. The second phase is the actual development of the system. The

    system utilizes NC coding generated from the software. The goal of the system is to show

    that an integrated reverse engineering and CAM machining system can make theremanufacturing process more automatic and efficient.

    Chow et al., [7] results are that of the comparison between the original parts and theduplicated parts. The samples were performed to evaluate the accuracy and efficiency of

    their concurrent reverse engineering system. They reported their findings of the errors of

    the overall integrated system were close to the calculated errors in the results of thereverse engineering feasibility study. The comparison table of the results and the time

    required to complete each step can be view in their paper [7].

    To summarize this section, Fan and Tsai [13] implemented a non-contact system that

    utilizes CCD cameras and laser triangulation for reverse engineering. Clark [8]implemented a non-contact system that works in conjunction with surfacing and

    inspection software. He also discussed some of the issues regarding the implementationof such systems for manufacturing purposes. Chow et al., [7], developed and

    implemented a process planning system that interfaces with a tightly coupled CAD

    modeling system and CAM tooling path. They demonstrate the accuracy and efficiencyof their laser-based reverse engineering system.

    2.5 General Constraints of Data Acquisition Techniques

    There are many practical problems with acquiring useable data, the major ones being:

    a. Calibrationb. Accuracyc. Accessibilityd. Occlusione. Fixture (placement)f. Multiple viewsg. Noise and incomplete datah. Statistical distributions of partsi. Surface finish

    Calibration is an essential part of setting up and operating a position-measuring device.

    Systematic sensing errors can occur through lens distortions, non-linear electronics in

    cameras, and similar sources. Any sensing must be calibrated so as to, first, accuratelydetermine parameters such as camera points and orientations, and second, to model and

    allow for as accurately as possible systematic sources of error. Most of the papers cited

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    Chapter 2: Acquisition Classifications and Related Works 14

    resulting data. However, it may be somewhat impractical to attempt to sample many parts

    from a population, and indeed, often only one is available.

    The final issue we bring up is surface finish of the part being measured. Smoothness and

    material coatings can dramatically affect the data acquisition process. Tactile or opticalmethods will produce more noise with a rough surface than a smooth one. Reflective

    coatings also can affect optical methods.

    Imagine an ideal scanner: the object is 'floating' in 3D spaces, so it is accessible from all

    directions. The data is captured in one coordinate system with high accuracy, with no

    need for noise filtering and registration. Possibly, the measurement is adaptive, i.e. morepoints are collected at highly curved surface portions, etc. Unfortunately, such a device

    does not exist at present. But, despite the practical problems discussed, it is possible to

    obtain large amounts of surface data in reasonably short periods of time even today using

    the methods described. Once the measured data is acquired, the process of recognition

    and model building can begin. The imperfect nature of the data, particularly inaccuracyand incompleteness, however, makes these steps fairly difficult as will be seen in the

    following sections.

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    Chapter 3: Surface Reconstruction 15

    3 SURFACE RECONSTRUCTION

    Obtaining a surface representation of objects and scenes has always been one of the mostchallenging and fundamental problems of 3D computer vision. In this section we will

    discuss the procedures to generating a successful 3D model of an object from single and

    multiple views. We will also discuss various surface reconstruction algorithms that have

    been developed and employed.

    3.1 Single Views and Data SegmentationAn iterative process for 3D reconstructions of surfaces in static environments is defined

    by the following steps (see also Figure 1.2, which depicts this process):

    1. Acquiring range images of the part2. Pre-processing acquired data (data segmentation)3. Data post-processing (data integration)4. Final 3D CAD model

    Range image acquisition is the first step of the process. In image acquisition, the CCD

    camera captures the scene. The result is a grey scale-image, which shows the intersectionbetween the laser plane and the object, which is a line. The image acquisition process

    yields a number of selected range images. The range images that are generated are of the

    view angles that the user has positioned the part for capture. Figure 3.1 shows a sequence

    of range images obtained from the IVP Range Scanner. The black area in between theblades of the water pump show occluded areas. These are depth range images. The

    purposes of taking multiple views are to eliminate the missing data from the water pump.

    The structured lighting system that we use for our project generates a different set of

    images when performing the image acquisition process. The images obtained from thissystem are color range images. These images also show the texture of the part or object

    that is being scanned. Although both system produces different types of images, they areall part of the acquisition step of the process. Figure 3.2 below is an example of the typeof images that are generated from the structured lighting system used in this project. The

    first scans from both systems are 2D representations of the real object. Although both the

    systems generate 2D views of the part, they are still considered single views. They aresingle views because one view cannot complete the reconstruction of the object.

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    Chapter 3: Surface Reconstruction 16

    (a) (b)

    (c) (d)

    (e) (f)

    Figure 3.1: (a) Original image of the water pump, (b) Sequenced

    single view range images of the bottom surface of the water pumpgenerated using our laser range scanner

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    Chapter 3: Surface Reconstruction 17

    (a) (b)

    (c) (d)

    Figure 3.2: Textured single view range images generated from the

    structured lighting system, (a) left view of the ramp, (b) front view of theramp, (c) back/left view of the ramp, (d) front/ right view of the ramp.

    Pre-processing the range images is the step proceeding data collection. Pre-processing is

    more commonly referred to as cleaning of the collected data. Pre-processing is applied to

    the single views individual before they are integrated and registered together. Asmentioned before pre-processing includes reducing erroneous data, filtering noise and

    filling holes that may have occurred as a result of occlusions. The additional steps of the

    process are described in the next few sections.

    3.2 Multiple View Integration and Registration

    For all objects and parts to be scanned, we require a geometric model of the whole

    objects surface, as stated above. Ideally, we would have the part floating in 3D space

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    Chapter 3: Surface Reconstruction 18

    (in a fixed position with a fixed orientation). This would be so that the scanner could

    move around the object from all sides to capture in a single coordinate system. Inpractice, the object will have to rest on some surface, so part of it is inaccessible to the

    scanner. Furthermore, if the scanner is fixed in position n, at any one time, it will be able

    to capture data from an even more limited region of the objects surface. Thus, generally,

    it will be necessary to combine multiple views taken with the object placed in differentorientations in front of the scanner.

    The more scans that are taken of the part, the longer the whole scanning process will take.

    For this reason, it is important to decide how many scans will be taken of the part or

    object. This can be determined based on the size and material make up the part. This is

    the second step of the post-processing stage for 3D reconstructions. The individual rangeimages must be aligned, or registered, into a common coordinate system so that they can

    be integrated into a single 3D model.

    In high-end systems, registration may be performed by accurate tracking. For instance,

    the scanner may be attached to a coordinate measurement machine that tracks its positionand orientation with a high degree of accuracy. Passive mechanical arms as well as robotshave been used. Automatic feature matching for computing the initial alignments is an

    active area of research (recent work includes [3, 6, 9-12, 24, 27, 35]). The most general

    formulation of the problem that makes no assumptions on type of features (in the range

    and/or associated intensity images) and initial approximate registration is extremely hardto solve. Approximate position and orientation of the scanner can be tracked with fairly

    inexpensive hardware in most situations, and can be used as a starting point to avoid

    searching a large parameter space.

    The goal of collecting multiple views of range images is to take these sets of range

    images and register the images. We want to take these sets of registered range images of

    the entire surface of the object and from these images produce a corresponding set ofparametric surface patches. Each range image is a dense sampling of the 3D geometry of

    the surface from a particular viewpoint. When reconstructing objects we want to have

    overlapping views of the object. The overlapping views should consist of the same area

    of the object being scanned. The main purpose for overlapping the different views is toomit occlusion in the object by matching various similar features on the object. Also,

    some views may have more details or some may have low resolution.

    In our system, the registration is a fairly easy process. Special software is use to match

    similar features and points on the different surfaces scanned. After the points have been

    selected for matching, they are matched based on the distance computed using the ICP(Iterative Closet Point) algorithm in the software. Figure 3.3 is an example of how our

    software registers two sets of range image view. Three or more points are matched based

    on similar corresponding features and feature location. This is commonly referred to as

    feature matching or extraction. Figure 3.3 a, contains one single view of the water pumpand Figure 3.3 b is another single view. Figure 3.3 c is the registration of both views

    overlapping. The overlapping red region shows how they are aligned in the same shell.

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    Chapter 3: Surface Reconstruction 19

    (a) (b)

    (c)

    Figure 3.3: Point cloud information of the side view of the water

    pump, (a) and (b) side view range images, (c) registered range imageof the two side views.

    The registration of the views may take several tries to achieve the optimal aligning of the

    images that is desired. After the two desired views are registered to each other at theideal feature locations, they can be merged together using various post-processing option

    offered in commercial software or by generated programs.

    3.3 Post-processing Registered Images

    After all the views of the object have been obtained, they are ready for post-processing.

    As mentioned above in Figure 1.2, post-processing of range images includes surface

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    Chapter 3: Surface Reconstruction 20

    smoothing and multiple view registration. This is the step before completing the 3D

    reconstruction of an object or part. Post-processing operations are often necessary toadapt the model resulting from scan integration to the application at hand. In other words,

    when performing the post-processing step, the registration process is made permanent.

    There are two techniques that are commonly used for this process. The first is surface-

    based methods and the second is volumetric methods.

    Surface-based methods create the surface by locally parameterization the surface andconnecting each point to its neighbors by local operations [2]. The partial connectivity

    that is implicit in the range images is made use of in other methods. For example, Turk

    and Levoy [29] zippering approach works by triangulating all the range scans

    individually. Redundant overlapping triangles are then eroded for removal from thepartial meshes. The intersecting regions are locally re-triangulated and then trimmed to

    create one seamless surface. The vertex positions are then readjusted to reduce error.

    Volumetric-based methods are useful for very large datasets. In volumetric methods, line

    of sight error compensation is done by computing a scalar field that approximates thesigned distance to the true surface [2]. This is based on a weighted average of distancesfrom sample points on the individual range image scans. Volumetric methods are also

    well suited to producing watertight models. The range images are used to carve out a

    spatial volume then, object definition can be obtained without holes in the surface. Solid

    modeling evolution from a series of range images can be demonstrated by Reed andAllen [23]. With the data obtained from each range image, they carve away the solid that

    lies between the scanner and each sampled data point.

    In our system, we use special software to perform the post-processing of the individual

    views. Rapidform2004 is 3D modeling software that we use to generate our complete 3D

    CAD models of our parts. We are allotted three options, surface merging, volumetricmerging and point cloud merging. The surface merging option merges shells of range

    views that have been aligned by the registration process into one united shell.Overlapping shell regions between the two separate shells are removed and neighboring

    boundaries are connected together with newly added polygons. One of the built in

    functions in the software is for merging the different range image views into one unitedshell.

    The Volumetric merging option merges multiple shells into a single shell by allocating

    their geometry information to a reference voxel model with a volumetric method. Avoxel is word created from two words (vector and pixel) to describe the 3D space of a

    pixel-based image. It is a volume element of a rectangular shape of the subject beingimaged. Volume based merging is useful when surface based meshing creates poor mergeresults. This polygon-merging tool helps you to merge scanned data with many holes and

    messy boundary, or bad aligned data.

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    Chapter 4: System Descriptions and Setup 21

    4. SYSTEM DESCRIPTIONS AND SETUP

    There are many profiling systems that can be used to capture data of objects for

    reconstruction. As mentioned in section 2, the different options fall under the category of

    contact and non-contact. To emphasize the objective of this project and the flexibility ofnon-contact options, we have chosen two different profiling systems for our project. Both

    profiling systems will be discussed in the next two sections.

    4.1 IVP Range Scanning Profiling System

    There are a number of 3D laser scanners commercially available. Depending on the

    specific needs for design, a particular type is chosen. The vast majority of 3D non-contactsystems employ triangulation. Triangulation systems are often classified as being either

    active or passive [8]. Passive methods such as stereo or photogrammetric systems use

    only cameras. Industrial settings however use systems that are active. In that they project

    some form of illumination onto the object and measure the position of the illumination onthe object [8]. This is done using some form of camera or light sensing electronics. A

    typical triangulation sensor diagram is shown below in Figure 4.1.

    A typical triangulation scheme projects a point or line (sheet) of laser light on an object,

    and observes the intersection of the object and laser through electronic cameras. Thisdescription describes a static system that only measures points where the laser line andobject meet. Too fully measure an object in 3D spaces, the sensor is moved in the x, y

    and z directions. This is to fully cover the area of the object.

    By sending laser beams radiated from the surface and received by CCD cameras, the 3Dlaser-scanning device can acquire the surface information of the part. Consisting of abeam projector radiating the laser beam, the laser scanner and a CCD camera sense the

    reflected beam from the surface. Laser stripe and point type are normally how laser

    beams can be categorized. A laser-type scanner radiates a line of laser beams, called a

    stripe, onto the surface so that several points can be acquired at once. In contrast to thestripe, a point type laser scanner obtains only one point at a time. Based on the

    configuration of the machine, laser-scanning devices can also be classified on those bases

    [15].

    The first profiling system that we employ to reconstruct a 3D model of an object is the

    IVP Range Scanning System. This system uses a laser stripe for acquisition. The IVPRanger SC386 is a laser triangulation scanner for range profiling using the MAPP family

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    Chapter 4: System Descriptions and Setup 22

    Smart Vision Cameras. Figure 4.2 is the IVP systems equipment and configuration used

    for data acquisition. Figure 4.2 b shows the Smart Camera and motor in more detail.

    Figure 4.1: Principle of a laser triangulation system. Arrangement for

    the IVP Smart Vision Camera and Laser

    The IVP Laser Scanner consists of a thin laser light and a camera that are used to obtainthe profile of objects. A thin laser light is projected onto the object and the CCD sensor

    from the camera detects the scan line (the peak of the reflected laser light). The profiles

    are displayed as a set of range images. The camera and laser are fixed on a stablestructure that moves in a horizontal direction. Figure 4.1 shows the angle placement of

    the camera to the laser light for the IVP Ranger System. The speed of the laser is

    controlled through the software that is associated with the system. The black box houses

    the motor for the system. The speed can be adjusted depending on the quality of scanningthat is to be achieved. Because the laser and camera are fixed on the same belt, they move

    at the same speed.

    For data to be acquired using the IVP Range Scanner, the system must be correctlycalibrated before every successful set of scanned data. Calibration of the IVP Range

    Scanner involves identifying the correct world coordinate data for the system so that themeasurements of the scanned object match both in real world data and transformed data.

    To calibrate the IVP a calibration grid is used to number all the coordinate data points.

    There are forty total points that must me identified, ranging from 0 to 39. Figure 4.3 is an

    example of the calibration grid used to calibrate the IVP Range Scanner. In the

    Range

    distance

    Objects

    Sensor

    (CCD

    Camera)Field of

    View

    Baseline distance

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    Chapter 4: System Descriptions and Setup 25

    the white line on the calibration grid in Figure 4.3. The goal of the object profile during

    calibration is to make sure that the entire object will be in the field-of-view of the cameraand laser during data acquisition. The field-of-view refers to the measured distance

    between the lasers light, the camera and object, (refer to Figure 4.2).

    4.2 Genex 3D FaceCam Profiling System

    As previously mentioned in section 2, structured lighting is the projection of a light

    pattern (plane, grid, or more complex shape) at a known angle onto an object [25].Scanning the object with the light pattern constructs 3D information of the shape of the

    object. This is the basic principle behind depth perception for machines. In most cases,structured lighting can be described as active triangulation. Active triangulation is a

    simple technique to achieve depth information with the help of structured lighting to scan

    a scene with a laser plane and to detect the location of the reflected stripes. The distortionalong the detected profile is used to compute the depth information.

    In our acquisition system, the stripe pattern is projected by multiple stripes at once ontothe scene. In order to distinguish between the stripes, they are coded (Coded Light

    Approach) with different brightness or different colors. The light grid has a rainbow color

    effect with the colors red, green and blue repeating. Figure 4.5 shows an example of astructured lighting grid projected onto our metallic ramp object.

    Figure 4.5: Structured light grid pattern projected on the

    ramp with a neutral tan background

    The Coded Light Approach (CLA) is an absolute measurement method of direct

    codification [5]. This method requires only a small number of images to obtain a full

    depth-image. This can be achieved with the sequence of projections using a grid of

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    Chapter 4: System Descriptions and Setup 26

    vertical lines (light or dark). Direct codification is usually constrained to neutral color

    objects or not highly saturated colors. All the lines are numbered from left to right. Inorder to achieve a pattern where each pixel coordinate can be directly obtained, it is ideal

    to use a large range of color values or reduce the range and introduce periodicity in the

    pattern. There are two common forms of coded light approaches, coding based on greylevels and coding based on color.

    In our second acquisition system for this project we use the Genex 3D FaceCam Systemfor reverse engineering purposes. Our purpose for selecting this system is to explore the

    accuracy and limitation of the machine for reverse engineering of automotive parts. In

    this system, three regular cameras, a digital camera and a single projector are used versesone single camera and single projector as mentioned earlier. The use of three cameras

    yields three separate images in the results, (see Figure 4.6 below). A right, left and

    centered image are obtain from the different cameras lens because of their view position

    in the system set up. Figure 4.6a is the Genex 3D FaceCam 500 System used in this

    project. In this system configuration, the projector is located under the center lens. Thedigital camera is located on top of the center lens.

    Figure 4.6: Genex 3D FaceCam System

    In our system, there is a specified distance of how far the object can be away from theGenex 3D FaceCam System when acquiring data. The total distance is 85 cm, with an

    allotted distance of 15 cm. Figure 4.7 shows the allotted distance when acquiring data

    from the Genex 3D FaceCam System. When scanning, the background should be aneutral color from the object. This is so that when eliminating the backgroundinformation, the two can be easily distinguished.

    The tan background can be seen clearly in Figure 4.8. Also in Figure 4.8, the coded light

    pattern projected onto the ramp is seen. This snapshot was taken directly from the user

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    Chapter 4: System Descriptions and Setup 27

    interface screen of the Genex 3D FaceCam system after the left side of the ramp data was

    collected. Three different view angles are captured with the three cameras to generate acomplete model of the left side of the image, as mentioned previously in this section.

    Figure 4.7: Distance specification for data collection

    Figure 4.8: Genex 3D FaceCam User Interface screen that displays theleft, center and right camera photos of the object

    Objectplacement

    Camera 3

    Camera 1

    Camera 2

    85 cm Background

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    Chapter 5: Results and Discussions 28

    5 RESULTS AND DISCUSSIONS

    In this section, we present results of our complete modeling process: data acquisition,

    data segmentation (pre-processing and post processing) and a 3D CAD model. We willshow a comparison of the actual data to the original data. We will also show a

    comparison of the data results obtained from both systems.

    5.1 Part Identification

    Our research is geared toward reconstruction of automotive parts. For our part selection,

    there was no predetermined criterion for selecting the parts. The main purpose was toselect parts that were ideal for our system setup and that could be easily rotated. For this

    project, there are three parts (objects) that have been identified, a water pump, a ramp and

    an arm pulley. All the selected parts are composed mostly of metal. The first part isshown below in Figure 5.1.

    The ramp object in Figure 5.1 is not classified as an automotive part. This specific ramp

    was specially designed for this project. It was designed on the basis of obtaining groundtruth information for the structured lighting system used in our experiments. The main

    reason for using a ramp shape is that it should be easy to measure and compare the real

    world dimensions to the CAD coordinate measurements after the complete 3D model hasbeen generated. The measurements of the ramp are 4 inches on the base for length and

    the height. The sloped side measures 3.5 inches, while the two shorter sides measure 1.4

    inches. The weight of the ramp is about 48 ounces (1,587 grams).

    The corner angles on the ramp are 90 degrees for the back and bottom surface. With thecurrent position of the ramp according to the photo below (Figure 5.1), the side with the

    holes is a perfect 45 degree angle. If the part is flipped to have the holes on top, then the

    angle is slightly higher at 47 degrees. This difference in angles does not pose any majordifference in obtaining a 3D model of the part.

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    Chapter 5: Results and Discussions 29

    (a) (b)

    (c)

    Figure 5.1: Photos of the ramp part (a) front and side views, (b) back and top views,

    (c) ramp measurements

    The water pump in Figure 5.2 was selected because of its complexity in shape and sizemeasurements. Due to the water pump having a non-symmetrical shape, there is no

    defined center of gravity. The center of gravity refers to if the positioning of the water

    pump placed on the opposed side, that it would not be balanced. The water pump top basescrew has a black tented color. The size of the water pump is 10 inches in length, 5.5

    inches wide on the largest area and 2.5 inches wide on the lower base. The total height of

    the water pump is about 4 inches on the larger half and 1.5 inches on the flat end.

    4 in.

    1.4 in.

    1.5 in.

    4 in.

    3.5 in.

    BackFront

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    Chapter 5: Results and Discussions 30

    (a) (b)

    Figure 5.2: Photos of the water pump part (a) top and side views (b)bottom view

    Figure 5.3 is the pulley arm that was selected as the third object to reconstruct. The pulley

    has a black circular ring located on the top end of the part. It also contains circular holes

    on the object that may cause pose a challenge in modeling due to occlusion. The pulleyarms measures about 11.5 inches in length. The width contains three different

    measurements. The pulleys black circle ring measures 1.5 inches while the center

    measures 2.5 inches and the lower base measures 1.5 inches. The height measurement is2.8 inches from the ground or working surface. All measurements are taken by hand.

    (a) (b)

    Figure 5.3: Photos of the pulley arm, (a) is the top view (b) is the bottom

    view

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    Chapter 5: Results and Discussions 31

    In the next section we will display our results from both the IVP Range Scanner and the

    Genex 3D FaceCam System. The results will be separated into three preliminary sections.Each section is a different set of scans, of the part, taken at different times. Each different

    set of scans were taken with different varying conditions. These conditions include theambient lighting from the room and outside lighting from the windows and doors.

    5.1 IVP Laser Range Data Results

    The first set of data results we will discuss are the results from the ramp using the IVPRange Scanner. The ramp posed a challenge while scanning because of the surface finish.

    The ramp has a highly reflective surface that caused areas of the ramp to be occludedwhile collecting the data at different orientations. Figure 5.4, a through d, are some

    CAD model examples of the ramp. These different images are different view angles taken

    of the ramp.

    (a) (b)

    (c) (d)

    Figure 5.4: CAD model images of the ramp (a) left/front

    view, (b) right side view, (c) bottom view, (d) back view

    In the CAD model images of Figure 5.4, the various views represent different angles ofthe ramp. Figure 5.4 a, is the right side of the ramp. This figure also shows part of the

    front view. Figure 5.4 b is the left side of the ramp. It also shows the other half of the

    front view from a different angle. Figure 5.4 c is the bottom view of the ramp and d is the

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    Chapter 5: Results and Discussions 32

    back view of the ramp showing the holes. Some of the details of the holes have been lost

    due to smoothing the surface of the ramp. More complete CAD models can be seenFigure 5.5. In these CAD images, the holes have been filled and the ramps surface has

    been smooth a second time. We then obtain a complete 3D CAD model of the ramp.

    (a) (b)

    (c) (d)

    Figure 5.5: 3D Ramp CAD models, (a) left/front side view, (b)

    back/left side view, (c) bottom view, (d) right/top/ front side view

    The second set of data collected using the IVP Range Scanner was of the water pump.

    The water pump image can be seen in Section 2.1, Figure 2.1. The lighting factor for this

    system does not affect the data as much as it affects the Genex System data. The reason is

    because a filter can be placed on the camera lens to filter out any unnecessary light. Forour experiments, we did not use the filter because the light source in our room

    environment was not a major issue.

    Figure 5.6 is the first attempt at reconstructing the bottom of the water pump. Figure 5.6

    a, is a merge of the different views to obtained this CAD model. After merging the views

    using the volumetric merge technique, overlapping areas created holes in the model. The

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    Chapter 5: Results and Discussions 33

    holes were filled to have a more complete water tight model. Part a, of Figure 5.6, is the

    solid mesh model and part b shows the point cloud information after the holes were filledin part a. The point cloud information usually shows the distance (spaces) between each

    point cloud, while the solid modeling technique shows a smooth continues image orview. The range images used to reconstruct the bottom surface can be seen in Figure 3.2

    above. Figure 5.6 c is the solid mesh model after the holes filling was applied.

    (a) (b)

    (c)

    Figure 5.6: Reconstructed bottom surface of the water pump, (a) Pointcloud information, (b) Solid mesh model, (c) smooth reconstructed mesh

    model

    Figure 5.7 a, is a reconstruction of the top view of the water pump. It is displayed in

    varying colors to show the depth information of the water pump. The blue represents thehighest part of the water pump while the orange color represents the surface that is

    closest to the ground. The green color represents the medium height level of the water

    pump. This image can be compared to the original photo in Figure 5.2. Figure 5.7 b, isthe depth information or rotated side view of the top angle. This view gives a more vivid

    description of the depth of the water pump. This top view does not have the side views of

    the water pump merged to it.

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    Chapter 5: Results and Discussions 34

    (a)

    (b)

    Figure 5.7: Reconstructed views of the water pump, (a)

    reconstructed top view (b) side view reconstruction

    In Figure 5.8, these images depict the attempts of modeling the right and left side of thewater pump. The areas that have occluded data must be taken at a different angle to the

    side views completely. Figure 5.8 a, and Figure b are the CAD model point cloud data

    information. Figure 5.8 part a as well as b show the height information in varying colors.Part c and d of Figure 5.8 are the second attempts at modeling the side views. The

    missing data can be seen clearly in these views. These views will be merged together to

    complete the side profile of the water pump.

    Figure 5.9, shows the complete 3D CAD model of the water pump obtained using the

    IVP Range scanner. In this figure, the front complete view of the water pump is shown.

    Again, the varying colors show the height changes in the water pump.

    The pulley arm results are displayed in Figure 5.10 b and a below. In this figure, the

    original photo image of the pulley arm is shown as well as the point cloud information.Figure 5.10 a, is the bottom side of the pulley arm and Figure 5.10 b is the top side. The

    color variations in the CAD model images are the height relative to the laser light. Tocomplete this model, additional view must be merged to the current views to have asuccessful 3D model of the pulley arm. This will eliminate all the holes and occluded

    areas of the pulley arm.

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    Chapter 5: Results and Discussions 35

    (a) (b)

    (c) (d)

    Figure 5.8: Cleaned point cloud data of the side views of the water pump

    taken with the IVP Range Scanner, (a) right view, (b) left view, (c) right view

    and (d) left view

    (a)

    (b)

    (c)

    Figure 5.9: Point cloud model of the water pump (a) CAD

    model showing height variations (b) top view, (c) back view

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    Chapter 5: Results and Discussions 37

    (a) (b)

    (c) (d)

    Figure 5.11: Textured ramp images using the Genex System, (a) back

    view, (b) right/ back side view, (c) left view and (d) front view

    In the images that were captured of the ramp, there is data missing in sections of the

    ramp. This is because these parts of the ramp reflected the light that was projected onto

    the object by the cameras during data capture. More views of the ramp must be taken atdifferent angles to fill in the missing data. Examples of the missing data can be seen

    clearly in the CAD models of the ramp displayed in Figure 5.12.

    Figure 5.12 are the first attempts of reconstructing the ramp using the Genex System. In

    this reconstruction some of the edge detail is missing (occluded) due to the edges being

    reflective. To solve this problem, more scans are taken of the ramp and merged to the

    already acquired range images. Figure 5.12 a, is the right side of the ramp. Figure 5.12 b,is the back view of the water pump. Part c and d of Figure 5.12 are the reconstructed front

    and left views. All the views can be compared to the original photo image in Figure 5.1

    of section 5.

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    Chapter 5: Results and Discussions 38

    (a) (b)

    (c) (d)

    Figure 5.12: Genex System reconstructed views of the water

    pump, (a) right view, (b) back view, (c) front view and (d) left

    view

    Figure 5.13 are the complete 3D CAD models of the ramp obtained from the Genex

    System. This is the second attempt at modeling the ramp. Compared to Figure 5.12, these

    CAD models images show the edge details that were missing and also have a smootherfinish. Figure 5.13 a, is the front CAD model view. Figure 5.13 b is the back CAD model

    view. In Figure 5.13 b, you can see that there is some miss alignment of the views

    causing the unleveled edge detail. With proper smoothing, this unevenness can be fixed.

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    Chapter 5: Results and Discussions 39

    (a) (b)

    (c) (d)

    Figure 5.13: Complete final 3D model of the ramp (a) front view (b)back view (c) side view (d) bottom view

    Figure 5.14: Water pump placed in box for neutral

    background

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    Chapter 5: Results and Discussions 40

    Figure 5.15 shows a few of the textured water pump range images that were captured

    using the Genex 3D System. The images contrast has been enhanced for better viewing ofthe details captured. These images also show some of the background from the box that

    was captured. The excess background does appear in the raw data files from the Genex3D System. It is easily cleaned away using some data reduction techniques in the

    software.

    (a) (b)

    (c) (d)

    Figure 5.15: Textured water pump images using the Genex System, (a) top

    view, (b) view of the bottom surface, (c) side view and (d) top view

    Figure 5.16 are the CAD models of the water pump. These CAD models displayed are

    the first attempts at reconstruction of the bottom surface of the water pump. The

    reconstruction efforts yielded successful results as far as most of the detail of the waterpump. The edges of the water pump are not refined or smooth. To obtain the detail of the

    holes along the outer brim of the water pump, more views can be merged to the current

    model. Figure 5.16 a, is the point cloud CAD model of the bottom surface. Figure 5.16, b

    through d are more detailed CAD models of the bottom surface. The blades of the bottomsurface can be seen clearly and the edges are more distinct as you view the images

    starting from b and ending at d. Also Figure 5.16 d has a smoother surface finishcompared to b and c.

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    Chapter 5: Results and Discussions 41

    (a) (b)

    (c) (d)

    Figure 5.16: Genex System reconstructed bottom surface of the water pump,(a) point cloud data, (b) solid mesh model, unsmooth surface textured and (d)

    smooth textured surface

    Figure 5.17 displays the first and second attempts of reconstructing the top surface ofwater pump. In the first attempt, there are occlusions in the center of the pump. The

    occluded data is recovered with more range scans, seen in the second attempt of Figure

    5.17 b. Some hole filling was also performed on the CAD model. The hole filling createsa more complete and smooth surface in the final model. However, with any smoothing

    technique that is used, some of the detail of the CAD model will be lost. The loss ofdetail can be seen on the outer brim on the holes of the model in Figure 5.17 b.

    Nevertheless, a good percentage of the detail, on the ridges, is still maintained.

    (a) (b)

    Figure 5.17: Genex System reconstructed top surface of the water pump,(a) surface reconstruction with missing data and (b) complete surface

    reconstruction with filled holes

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    Chapter 5: Results and Discussions 42

    Figure 5.18 below, are some CAD models of the side views of the water pump using the

    Genex System. Part a, is the right side and part b is the left side. A better angle of theviews can be seen in parts c and d of the image. These images show more detail of the

    sides of the water pump. The views of the right and left side are used to complete the topand bottom surfaces of the water pump. When merged together, they show a more

    complete top and side view. As the CAD images are viewed from Figure 5.18 a, to f, itbecomes more distinct of how the views are merged together to create a complete model.

    Figure 5.18 a, is the right side view, and b is the left side view.

    Figure 5.18 c is another right side view that is angled to show how much of the detail is

    captured when the part is repositioned at a new orientation. Figure 5.18 d is a merging of

    two different views to complete the left side of the water pump. Once the left side iscomplete, the right side CAD model can be merged onto the existing model. The final

    merging can be seen below, in Figure 5.19.

    The first two images in Figure 5.19 are the original photos of the water pump. The photos

    are placed there for comparison purposes to the 3D model in c through f. Figure 5.19

    shows four different views of the water pump. The views show the front, right side, left

    side and back of the water pump. This 3D CAD model shows all the details of theoriginal water pump.

    As compared to the original water pump image, the details of the side profile of the CADmodel still contains some unsmooth surfaces. By applying the smoothing technique again

    to the model, the unsmooth surfaces will even out. There is however a drawback tosmooth the surface several times. This drawback is that the smaller details of the edges

    and raised surfaces may also be smooth. This extra smoothing results in the loss ofimportant details. The next section is a conclusion of this paper.

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    Chapter 5: Results and Discussions 43

    (a) (b)

    (c) (d)

    (d) (f)

    Figure 5.18: Genex System reconstructed views of the water pump, (a)right view, (b) left view, (c) right/angled view, (d) left/angled view, (e)

    front/angled view and (f) top/left angled view

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    Chapter 5: Results and Discussions 44

    (a) (b)

    (c) (d)

    (e) (f)

    Figure 5.19: (a) and (b) Water pump photo image, (c f) Final 3D CAD

    model views of the water pump.

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    Chapter 5: Results and Discussions 45

    5.3 Comparison: IVP Laser Range Scanner and Genex 3D

    FaceCam Data

    In this section, a comparison of the IVP Range Scanner and the Genex 3D FaceCam isdone. We first compare the ramp models generated from both systems. Figure 5.20 are

    rotated examples of the standard deviation calculated from both ramp models. In this

    figure, the IVP ramp and the Genex ramp are overlapped on top of each other to comparethe surface differences. The blue regions represent the surfaces that are touching.

    Figure 5.20: Standard deviation calculations of the

    IVP ramp overlapped with the Genex ramp.

    a b

    c d

    e

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    Chapter 5: Results and Discussions 46

    Meaning that, in the overlapping surfaces the blue region represents a distance of 0 mmbetween the two surfaces. The position 0.0 means zero positioning of the coordinate

    distance. The red region represents a maximum deviation of 3.91693 mm. The standarddeviation is calculated by registering the two models into one shell and then using the

    standard deviation option in the software to perform the calculations. The distance valuesshown here may be a little different from the ones shown in real time when the deviation

    is calculated. This is because the picking of the points are manual and leave error for not

    always being precise. The average of the deviation between the surfaces is calculated tobe about .9 mm distance apart shown in Figure 5.21.

    Figure 5.21: Standard deviation of the IVP ramp overlapped with the Genex ramp

    model

    Table 1 below shows the derived measurements of the ramp compared to actualmeasurements. The derived measurements are from the 3D CAD model that was

    generated from the data collected using both systems. The measurements are off from the

    actual measurements. This is because the points used to determine the measurements arechosen manually. Figure 5.22 is to show the comparison with the measurements of the

    sides used in the table.

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    Chapter 5: Results and Discussions 47

    Figure 5.22: Simulated ramp. This picture

    shows the measurements of the ramp.

    Table 1

    Actual Ramp

    Measurements

    IVP Laser Range

    Scanner Results

    Genex 3D FaceCam

    Results

    width: 3 in (76.2 mm) 2.95 in (74.93 mm) 2.93 in (74.422 mm)

    length: 4 in (101.6 mm) 3.62 in (91.948 mm) 3.6 in (91.44 mm)

    height: 4 in (101.6 mm) 3.86 in (98.044 mm) 3.78 in (96.012 mm)

    1.5 in. (38.1 mm) 1.41 in. (35.814 mm) 1.3 in. (33.02 mm)

    Table 2 is a comparison of both the IVP Range Scanner and the Genex 3D FaceCamsystem. This comparisons table was generated based on observations of the systems

    performance, and overall flexibility to acquire data. The Genex system has the advantage

    of fewer views when the object is of high complexity. Fewer views means, there is lessocclusion in the data. Take the water pump for example (Figure 5.2), because the side of

    the water pump is small in area, the Genex system is able to capture the side and either

    some area of the top and bottom of the water pump. This makes it easier whenoverlapping the views to recreate the object. The IVP Range Scanner requires fixing the

    object at particular angles to capture the same profile. This could require placing the

    object on a smaller object that is able to absorb the laser light or be hidden from the laser.

    The flexibility of both systems is the same in comparison, in that they can both be easily

    moved. The contrast is that the IVP Range Scanner requires some assembly of the

    system. This is primarily because the current setup is built in house according to ourspecifications. Both systems are user friendly as far as setup and data collection is

    required. The contrast in the systems is that when setting up the IVP Range Scanner to

    1.5 in

    3 in

    4 in

    4 in

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    Chapter 5: Results and Discussions 48

    acquire data, the system must be calibrated before any data is collected. As mentioned

    previously, in the process of calibrating the IVP, the world coordinate points have to beinput manually to make sure that the data is transformed properly into the 3D software.

    Also the data must have some y-scaling applied to it to make sure that the acquired datameasurements are similar to the real data of the water pump.

    After the scans of the object have been collected, the IVP creates a shadow affect of the

    object. This is because of the laser light reflecting the object. The Genex 3D FaceCam

    does not have this problem. This is because the Genex system captures data from thefront of the object as opposed to on an angle like the IVP Range Scanner. However, what

    does affect the Genex system is the ambient lighting that is in the room. The ambient

    lighting (if there is too much present) can saturate the object causing distortion to the dataacquired. Both systems pose the same problem when registration of different views

    comes into play. The way to over come this problem is the next best view of capturing

    the object from certain angles where there is overlap of the surfaces.

    The noise in the images from both systems is not a big factor when collecting data. The

    noise for the IVP can be reduced as long as the speed of the scan is sufficient to have a

    smooth surface. The achieve a smooth surface, the speed should be slowed as to avoidridged jerks of the laser as it scans the profile of the object. The Genex system has the

    option of reducing noise when the post-processing is performed on the collected data.

    Table 2

    IVP Range Scanner Genex System

    1 Creates a shadow effect when scanning 1 Creates a distortion from the ambient

    lighting2 Difficult in position of objects

    (complexity in shape)

    2 Difficult in position of objects with

    current system set up

    3 Needs more overlapping views 3 Needs fewer views to complete 3D

    object

    4 Calibration of the system and y scaling

    after scans

    4 No calibrating or y scaling

    5 Manual input of the world coordinateinformation

    5 No manual conversion of the worldcoordinate data

    6 If calibration is not correct, then needs

    to be redone

    6 Overall system is user friendly

    7 Portability of the system is not optional 7 Portability is manageable

    8 Difficulty registration when notsymmetrical

    8 Difficulty registration when notsymmetrical

    9 Less noise in previous scans with newsetup

    9 Overall s