Abstract
3D local shapes are a critical cue for object recognition in 3D point clouds. This paper presents an instance-based 3D object recognition method via informative and discriminative shape primitives. We propose a shape primitive model that measures geometrical informativity and discriminativity of 3D local shapes of an object. Discriminative shape primitives of the object are extracted automatically by model parameter optimization. We achieve object recognition from 2.5/3D scenes via shape primitive classification and recover the 3D poses of the identified objects simultaneously. The effectiveness and the robustness of the proposed method were verified on popular instance-based 3D object recognition datasets. The experimental results show that the proposed method outperforms some existing instance-based 3D object recognition pipelines in the presence of noise, varying resolutions, clutter and occlusion.
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