This paper proposes a novel approach to recognize object and scene categories in depth images. We introduce a Bag of Words (BoW) representation in 3D, the Selective 3D Spatial Pyramid Matching Kernel (3DSPMK). It starts quantizing 3D local descriptors, computed from point clouds, to build a vocabulary of 3D visual words. This codebook is used to build the 3DSPMK, which starts partitioning a working volume into fine sub-volumes, and computing a hierarchical weighted sum of histogram intersections of visual words at each level of the 3D pyramid structure. With the aim of increasing both the classification accuracy and the computational efficiency of the kernel, we propose two selective hierarchical volume decomposition strategies, based on representative and discriminative sub-volume selection processes, which drastically reduce the pyramid to consider. Results on different RGBD datasets show that our approaches obtain state-of-the-art results for both object recognition and scene categorization. 
Recognizing in the depth: Selective 3D Spatial Pyramid Matching Kernel for object and scene categorization