Co-segmentation of 3D shapes via multi-view spectral clustering

Co-segmentation of 3D shapes in the same category is an intensive topic in computer graphics. In this paper, we present an unsupervised method to segment a set of meshes into corresponding parts in a consistent manner. Given the over-segmented patches as input, the co-segmentation result is generated by grouping them. In contrast to the previous method, we formulate the problem as a multi-view spectral clustering task by co-training a set of affinity matrices derived from different shape descriptors. For each shape descriptor, the affinity matrix is constructed via combining low-rankness and sparse representation. The integration of multiple features makes our method tolerate the large geometry and topology variations among the 3D meshes in a set. Moreover, the low-rank and sparse representation can capture not only the global structure but also the local relationship, which demonstrate robust to outliers. The experimental results show that our approach successfully segments each category in the benchmark dataset into corresponding parts and generates more reliable results compared with the state-of-the-art.