PPConv - polypod convolution for 3D point cloud description.
SA '18: SIGGRAPH Asia 2018 Tokyo Japan December, 2018(2018)
摘要
3D point cloud is a collection of unordered sparse 3D points that is different from densely structured color image. Therefore, applying a fixed structure of deep learning network on 3D point cloud is a challenging task in computer vision and graphics problems. Recently, researchers have proposed deep learning methods for 3D point cloud based on data conversion or simplification. However, they lose either local 3D shape information for the simplicity of method or geometric locality for using array as an input. In this paper we propose a new convolution technique, named Polypod convolution, for 3D point cloud description that is distribution independent and maintains both local and global 3D shapes. Quantitative and qualitative evaluation results show the potential of our new network for 3D point cloud based deep learning applications.
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关键词
3d geometric, point cloud, neural network, 3d shape
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