mesa lab two papers in icfda14 guimei zhang mesa lab mesa (mechatronics, embedded systems and...

MESA LABMESA LABMESA LABMESA LAB

Two papers in Two papers in icfda14icfda14

Guimei ZhangMESA MESA (Mechatronics, Embedded Systems and Automation)LABLAB

School of Engineering,University of California, Merced

E: [email protected] Phone:209-658-4838Lab: CAS Eng 820 (T: 228-4398)

June 30, 2014. Monday 4:00-6:00 PMApplied Fractional Calculus Workshop Series @ MESA Lab @ UCMerced


The first paper

06/30/2014 AFC Workshop Series @ MESALAB @ UCMerced

Slide-2/1024

Paper title:


Motivation

1. Detect and localize objects in single view RGB images, the environments containing arbitrary illumination, much clutter for the purpose of autonomous grasping.

2. Objects can be of arbitrary color and interior texture,

thus, we assume knowledge of only their 3D model

without any appearance/texture information.

3. Using 3D models makes an object detector immune to

intra-class texture variations.


Motivation

• In this paper, we address the problem of a robot

grasping 3D objects of known 3D shape from their

projections in single images of cluttered scenes.

• We further abstract the 3D model by only using its 2D

Contour and thus detection is driven by the shape of

the 3D object’s projected occluding boundary.


Main achievements


Overview of the proposed approach

a) The input image

b) Edge image used gPb method

c) The hypothesis bounding box (red) is segmented into

superpixels. d) The set of superpixels with the closest distance to the model contour is selected.

e)three textured synthetic views of the final pose estimate are shown.

MESA LABMESA LABMESA LABMESA LABHow to do

1. 3D model acquisition and rendering

(use a low-cost RGB-D depth sensor and a dense surface

reconstruction algorithm, KinectFusion)

2. Image feature (edge)

3. Object detection

4. Shape descriptor

5. Shape verification for contour extraction

6. Pose estimation (image registration)

MESA LABMESA LABMESA LABMESA LABExample

(a) bounding boxes ordered by the detection score ( b) Corresponding

pose output (c) Segmentation of top scored (d) Foreground mask selected by shape

(e) Three iterations in pose refinement (f) Visualization of PR2 model with the Kinect point cloud (g) Another view of the same scene


The second paperPaper title:


Motivation

Problems:1. big and complex scenes, there must be many

3D point clouds, which need human label and will result in to spend much time.

2. Considering the bias problem of model learning caused by bias accumulation in a sample collection


Therefore, this paper proposes a semi-supervised

method to learn category models from unlabeled

“big point cloud data”. The algorithm only requires

to label a small number of object seeds in each

object category to start the model learning, as shown

in Fig. 1. Such design saves both the manual labeling

and computation cost to satisfy the model-mining

efficiency requirement.

Motivation


The main contributions

1. To the best of our knowledge, this is the first proposal for an efficient mining of category models from “big point cloud data”. With limited computation and human labeling, the method is oriented toward an efficient construction of a category model base.

2. A multiple-model strategy is proposed as a solution to the bias problem, and provides several discrete and selective category boundaries.


Expermient

Model-based point labeling results. Different colors indicate different categories, i.e. wall (green), tree (red), and street (blue).


Thanks

mesa lab two papers in icfda14 guimei zhang mesa lab mesa (mechatronics, embedded systems and...

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