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A Probabilistic Framework for 3D Visual Object Representation
October 2009 (vol. 31 no. 10)
pp. 1790-1803
We present an object representation framework that encodes probabilistic spatial relations between 3D features and organizes these features in a hierarchy. Features at the bottom of the hierarchy are bound to local 3D descriptors. Higher level features recursively encode probabilistic spatial configurations of more elementary features. The hierarchy is implemented in a Markov network. Detection is carried out by a belief propagation algorithm, which infers the pose of high-level features from local evidence and reinforces local evidence from globally consistent knowledge, effectively producing a likelihood for the pose of the object in the detection scene. We also present a simple learning algorithm that autonomously builds hierarchies from local object descriptors. We explain how to use our framework to estimate the pose of a known object in an unknown scene. Experiments demonstrate the robustness of hierarchies to input noise, viewpoint changes, and occlusions.
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Index Terms:
Computer vision, 3D object representation, pose estimation, nonparametric belief propagation.
Citation:
Renaud Detry, Nicolas Pugeault, Justus H. Piater, "A Probabilistic Framework for 3D Visual Object Representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1790-1803, Mar. 2009, doi:10.1109/TPAMI.2009.64
