automated 3d model construction for urban environments

Automated 3D Model Construction for Urban

Environments

Christian Frueh John Flynn Avideh Zakhor

Next Generation 4D Distributed Modeling and Visualization

University of California, Berkeley

June 13, 2002

Presentation Overview

Introduction

Ground based modeling

- Mesh processing

Airborne modeling

- Aerial photos

- Airborne laser scans

3D Model Fusion

Rendering

Conclusion and Future Work

Introduction

Goal: Generate 3D model of a city for virtual walk/drive/fly-thrus and simulations

• Fast• Automated• Photorealistic

• 3D model of street scenery & building façades

• highly detailed

needed:

• 3D Model of terrain and buildings tops & sides

• coarse resolution

For Fly-Thru: For Walk/Drive-Thru:

Introduction

3D model of building façades

3D Model of terrain and building tops

Airborne Modeling•Laser scans/images from

plane

Ground Based Modeling• Laser scans & images from

acquisition vehicle

Complete 3D City Model

Fusion

Airborne Modeling

Available Data:

• Aerial Photos

• Airborne laser scans

Acquisition of terrain shape and top-view building geometry

Goal:

Geometry: 2 approaches:

I) stereo matching of photos

Texture: from aerial photos

II) airborne laser scans

Airborne Modeling

Stereo photo pairs from city/urban areas, ~ 60% overlap

Approach I : Stereo Matching

• Camera parameter computation,

• Matching,• Distortion reduction, • Model generation

• Segmentation

Manual: Automated:

Semi-Automatic

(last year)

Stereo Matching

Stereo pair from downtown Berkeley and the estimated disparity after removing perspective distortions

Stereo Matching Results

Downtown Oakland

Airborne Modeling

Scanning city from plane

point cloud

• Resolution 1 scan point/m2

• Berkeley: 40 Million scan points

Approach II: Airborne Laser Scans

Airborne Laser Scans

• Re-sampling point cloud

• Sorting into grid

• Filling holes

usable for:

Map-like height field

• Mesh Generation• Monte Carlo Localization

Textured Mesh Generation

1. Connecting grid vertices to mesh

2. Applying Q-slim simplification

3. Texture mapping:

• Semi-automatic• Manual selection of few correspondence

points: 10 mins/entire Berkeley• Automated camera pose estimation• Automated computation of texture for mesh

Airborne Model

East Berkeley campus with campanile

Airborne Model

http://www-video.eecs.berkeley.edu/~frueh/3d/airborne/

Downtown Berkeley

Ground Based Modeling

buildings

truck

x

z

y

2D laser v

u

Acquisition vehicleTruck with rack:• 2 fast 2D laser scanners• digital camera

Acquisition of highly detailed 3D building façade models

Scanning setup• vertical 2D laser scanner for

geometry capture • horizontal scanner for pose

estimation

Goal:

Scan Matching & Initial Path Computation

Horizontal laser scans:• Continuously captured during vehicle motion

t = t0

t = t1

• Overlap

Relative position estimation by scan-to-scan matching

Scan matching

Translation (u,v)Rotation

(u, v)

Adding relative steps (ui, vi, i)

path (xi,yi,i)

(u1, v1, 1)(u2, v2, 2)

…

(ui, vi, i)

(ui-1, vi-1, i-1)

3 DOF pose (x, y, yaw)

6 DOF Pose Estimation From Images

Scan matching cannot estimate vertical motion- Small bumps and rolls - Slopes in hill areas

Full 6 DOF pose of the vehicle is important; affects:- Future processing of the 3D and intensity data - Texture mapping of the resulting 3D models

Extend initial 3 DOF pose by deriving missing 3 DOF (z, pitch, roll) from images

6 DOF Pose Estimation From Images

Central idea: photo-consistency

• Each 3D scan point can be projected into images using initial 3 DOF pose

• If pose estimate is correct, point should appear the same in all images

• Use discrepancies in projected position of 3D points within multiple images to solve for the full pose

6 DOF Pose Estimation – Algorithm

3DOF of laser as initial estimate Project scan points into both images If not consistent, use image correlation to

find correct projection Ransac used for robustness

6 DOF Pose Estimation – Results

with 3 DOF pose

with 6 DOF pose

6 DOF Pose Estimation – Results

Model

Number

Original

Number of

Seams

Completely

Removed

Less

notic able No Change Worse

1 48 42 0 4 2

2 15 8 0 7 0

3 29 25 2 2 0

4 24 18 1 5 0

5 33 24 0 9 0

6 18 15 0 3 0

7 34 28 1 3 2

Total 201 160 4 33 4

Perc entage 80 2 16 2

Monte Carlo Localization (1)

Previously: Global 3 DOF pose correction using aerial photography

b) path after MCL correctiona) path before MCL correction

After correction, points fit to edges of aerial image


Extend MCL to work with airborne laser data and 6 DOF pose

No perspective shifts of building tops, no shadow lines

Use terrain shape to estimate z coordinate of truck

• Fewer particles necessary, increased computation speed

• Significantly higher accuracy near high buildings and tree areas

• Correct additional DOF for vehicle pose (z, pitch, roll)

• Modeling not restricted to flat areas

Now:


Resulting corrected path overlaid with airborne laser height field

Track global 3D position of vehicle to correct relative 6 DOF motion estimates

Path Segmentation

vertical scans: 107,082scan points: ~ 15 million

24 mins, 6769 meters

Too large to process as one block!

• Segment path into quasi-linear pieces

• Cut path at curves and empty areas

• Remove redundant segments

Path Segmentation

Resulting path segments overlaid with edges of airborne laser height map

Simple Mesh Generation

Simple Mesh Generation

Triangulate

Point cloud Mesh

Problem:• Partially captured

foreground objects • erroneous scan points

due to glass reflection

Side views look “noisy”

Remove foreground: extract facades

Façade Extraction and Processing (1)

1. Transform path segment into depth image

2. Histogram analysis over vertical scans

scan n

r

depth

0

5

10

15

20

25

1 6 11 16 21 26 31 36

split depth

depth

local minimum

main depth

scanner

split depth

ground points

depth value sn,υ for

a scan point Pn,υ

main depth


3. Separate depth image into 2 layers:

background =building facades

foreground =trees, cars etc.


4. Process background layer:

• Fill areas occluded by foreground objects by extending geometry from boundaries

•Horizontal, vertical, planar interpolation, RANSAC

• Detect and remove invalid scan points

• Apply segmentation

• Fill remaining holes in large segments

• Final result: “clean” background layer

• Remove isolated segments

Façade Extraction – Examples (1)

without processing with processing


with processing

without processing


without processing

with processing

Facade Processing

Foreground Removal

Mesh Generation

Downtown Berkeley

Automatic Texture Mapping (1)

Camera calibrated and synchronized with laser scanners

Transformation matrix between camera image and laser scan vertices can be computed

1. Project geometry into images

2. Mark occluding foreground objects in image

3. For each background triangle:

Search pictures in which triangle is not occluded, and texture with corresponding picture area


Efficient representation: texture atlas

Copy texture of all triangles into “mosaic” image

Typical texture reduction: factor 8..12


Large foreground objects: Some of the filled-in triangles are not visible in any image!

“texture holes” in the atlas

Texture synthesis: preliminary

• Mark holes corresponding to non-textured triangles in the atlas

• Search the image for areas matching the hole boundaries

• Fill the hole by copying missing pixels from these image


Texture holes

marked

Texture holes filled

Ground Based Modeling - Results

Façade models of downtown Berkeley

Model Fusion

Goal:Fusion of ground based and airborne model to one single model

1. Registration of models

2. Combining the registered meshes

Model Fusion:

Façade model

Airborne model

Registration of Models

Models are already registered with each via Monte-Carlo-Localization !

Which model to use where?

Preparing Ground Based Models

Intersect path segments with each other Remove degenerated, redundant triangles in overlapping areas

original mesh

redundant triangles removed

Preparing Airborne Model

Remove facades in airborne model where ground based geometry is available

Add ground based façades Fill remaining gaps with a “blend

mesh” to hide model transitions

Ground based model has 5-10 times higher resolution


Initial airborne model


Remove facades where ground based geometry is available

Combining Models

Add ground based façade models

Combining Models

Fill remaining gaps with a “blend mesh” to hide model transitions

Model Fusion - Results

Rendering

Subdivide model and create multiple level-of-details (LOD)

Generate scene graph, decide which LOD to render when

• Up to 270,000 triangles, 20 MB texture per path segment

• 4.28 million triangles, 348 MB texture for 4 downtown blocks

Ground based models:

• Difficult to render interactively!

Multiple LODs for façade meshes

Highest LOD

• Geometry: 10%

• Texture: 25%

Lower LOD

of original mesh

•Qslim mesh simplification

•Texture subsampling

Façade Model Subdivision for Rendering

Subdivide 2 highest LODs of façade meshes along cut planes

Sub-scene

LOD 2

Path segment

Global scene

LOD 0LOD 1

LOD 0LOD 1

LOD 0LOD 1

Submesh

Submesh

Submesh

Interactive Rendering

Downtown blocks: Interactive rendering with web-based browser!

Future Work

Resolution enhancement and post-processing of LIDAR data

Devise new data acquisition system and algorithms to capture - both sides of street simultaneously- texture for upper parts of tall buildings

Include foreground objects in model Add temporal component to dynamically

update models Compact representation Interactive rendering

automated 3d model construction for urban environments

Documents

d model construction

d scan point

d points

d modelsextend initial

d distributed modeling

yaw6 dof

i3 dof

images6 dof