Construction of Feature Tensor Descriptor and Self-Similarity Analysis for 3D Point Cloud Models

Abstract

<p indent=0mm>Local self-similarity of 3D model is a fundamental problem in the shape analysis. The construction of a local shape descriptor is very important to the final result of self-similarity analysis. To solve this problem, a self-similarity analysis method based on the tensor fusion feature descriptor is proposed. Firstly, the shape diameter function (SDF) of a point cloud model is approximately calculated by using relevant facets and antipodal points. Then, spectral clustering is used to segment the model into sub-blocks, and the three-dimensional feature tensor is constructed from the SDF, shape index (SI) and Gauss curvature (GS) matrix of <italic>K</italic>NN neighborhood points. Finally, the shape descriptor is obtained by constructing the mapping with the tensor norm, and then the similarity measure is defined and the self-similarity between the sub-blocks of the model is analyzed. Several state-of-the-art methods (including partial matching and saliency detection) are tested. In terms of not only the visual effect, but also the similarity measure and the relative errors, the results show that this method can effectively describe the shape and improves the recognition accuracy of similar sub-blocks of a point cloud model.