With the innovation in 3D modeling software, more and more 3D models are becoming available in recent decades. To facilitate efficient retrieval and search of large 3D model databases, an effective shape classification algorithm is badly in need. In this paper, we propose a new feature descriptor named Principal Thickness Images (PTI) that encodes the boundary surface and the voxelized constituents of a 3D shape into three gray-scale images. With the support of PTI, we extend the kernel sparse representationbased classification from 2D case to non-rigid 3D models. Our classification algorithm inherits the robustness of kernel sparse representation and is able to achieve a high success rate and strong reliability on non-rigid models from the SHREC'11 non-rigid 3D models dataset. Numerous tests demonstrate superior performance of the proposed method over previous 3D shape classification approaches.
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We thank the anonymous reviewers for their valuable comments and suggestions. This work is supported by National Natural Science Foundation of China (11226328, 61222206, 11526212, 61300168, 61303144, 31302231), National Science Foundation CAREER Award (IIS-1350330), Natural Science Foundation of Zhejiang Province (LY13F020018), Opening Foundation of Zhejiang Provincial Top Key Discipline (XKXL1406), Natural Science Foundation of Ningbo City Grant (2015A610123), andOne Hundred Talent Project of the Chinese Academy of Sciences (Ligang-Liu).