mesa lab two papers in icfda14 guimei zhang mesa lab mesa (mechatronics, embedded systems and...
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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
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The first paper
06/30/2014 AFC Workshop Series @ MESALAB @ UCMerced
Slide-2/1024
Paper title:
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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.
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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.
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Main achievements
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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
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The second paperPaper title:
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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
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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
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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.
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Expermient
Model-based point labeling results. Different colors indicate different categories, i.e. wall (green), tree (red), and street (blue).
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Thanks