Multiscale shape context and re-ranking for deformable shape retrieval

This paper proposes a distinct multiscale shape context (MSC) method for isometric 3D shape description and retrieval. For each feature point, a MSC descriptor is devised to capture the multiple spatial information on the basis of the intrinsic shape context, which is advantageous in solving the domain offset deficiency for intra-class shapes. Different from the traditional shape context method, the MSC descriptor is built based on the charts without angular bins and the shape distributions in local domains, which makes it not only simple but also efficient. To reduce the cost of shape representation, we detect a sparse set of feature points and design an improved bags-of-word model to encode the MSC descriptors. For retrieval improvement, an efficient while robust re-ranking algorithm by metric mapping is designed to alleviate the errors of the feature space. Finally, the experimental results have demonstrated significant performance gains on two public benchmarks.