For the problem of object category recognition, we have studied different families of descriptors exploiting RGB and 3D information. We have proven practically that 3D shape-based descriptors are more suitable for this type of recognition due to low shape intra-class variance, as opposed to texture-based. Performance evaluation on training-set subsampling, suggests that the viewpoint invariance characteristics of 3D descriptors, favors significantly these descriptors while invariant SIFT descriptors can be ambiguous. In addition, we have also shown how an efficient Naive Bayes Nearest Neighbor (NBNN) classifier can scale to a large hierarchical RGB-D Object Dataset and achieve, with a single descriptor type, an accuracy close to state-of-the-art learning-based approaches using combined descriptors.

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NBNN,3D surface descriptors,RGB-D,Feature matching,Object category recognition

• Computer and Information Sciences - Natural Sciences