Occlusion remains a major hindrance for automatic recognition of 3-D objects. In this paper, we address the occlusion problem in the context of polyhedral object recognition from range data. A novel approach is presented for object recognition based on sound occlusion-guided reasoning for feature distortion analysis and perceptual organization. This type of reasoning enables us to maximize the amount of information extracted from the scene data, thus leading to robust and efficient recognition. The proposed approach is based on a multi-stage matching process, which attempts to recognize scene objects according to their order in the occlusion hierarchy (i.e., an object is recognized before those that are occluded by it). Such a strategy helps in resolving some
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