Area-weighted surface normals for 3D object recognition
Pattern Recognition（2012）
摘要
This paper presents a method for feature-based 3D object recognition in cluttered scenes. It deals with the problem of non-uniform sampling density which is inherent in typical range sensing methods. We suggest a method operating on polygonal meshes which overcomes the problem by exploiting surface area in both establishing local frames and creating feature descriptors. The method is able to recognize even highly occluded objects and outperforms state of the art in terms of recognition rate on a standard publicly available dataset.
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关键词
feature extraction,mesh generation,object recognition,area-weighted surface normals,cluttered scenes,feature descriptor creation,feature-based 3D object recognition,highly occluded object recognition,local frames,nonuniform sampling density problem,polygonal mesh,range sensing methods,surface area exploitation
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