Object Pose Detection in Range Scan Data

We address the problem of detecting complex articulated objects and their pose in 3D range scan data. This task is very difficult when the orientation of the object is unknown, and occlusion and clutter are present in the scene. To address the problem, we design an efficient probabilistic framework, based on the articulated model of an object, which combines multiple information sources. Our framework enforces that the surfaces and edge discontinuities of model parts are matched well in the scene while respecting the rules of occlusion, that joint constraints and angles are maintained, and that object parts don't intersect. Our approach starts by using low-level detectors to suggest part placement hypotheses. In a hypothesis enrichment phase, these original hypotheses are used to generate likely placement suggestions for their neighboring parts. The probabilities over the possible part placement configurations are computed using efficient OpenGL rendering. Loopy belief propagatio...

• Type: Conference
• Year: 2006
• Where: CVPR
• Authors: Jim Rodgers, Dragomir Anguelov, Hoi-Cheung Pang, Daphne Koller
• Keywords: Articulated Model, Complex Articulated Objects, Computer Vision, CVPR 2006, Likely Object Configuration, Likely Placement Suggestions, Object Parts