Differentiable Surface Splatting for Point-based Geometry Processing

Differentiable Surface Splatting for Point-based Geometry Processing

Wang Yifan yifan.wang@inf.ethz.ch ETH ZurichSwitzerland Felice Serena fserena@student.ethz.ch ETH ZurichSwitzerland Shihao Wu shihao.wu@inf.ethz.ch ETH ZurichSwitzerland Cengiz Öztireli cengiz.oztireli@disneyresearch.com Disney Research ZurichSwitzerland  and  Olga Sorkine-Hornung sorkine@inf.ethz.ch ETH ZurichSwitzerland
Abstract.

We propose Differentiable Surface Splatting (DSS), a high-fidelity differentiable renderer for point clouds. Gradients for point locations and normals are carefully designed to handle discontinuities of the rendering function. Regularization terms are introduced to ensure uniform distribution of the points on the underlying surface. We demonstrate applications of DSS to inverse rendering for geometry synthesis and denoising, where large scale topological changes, as well as small scale detail modifications, are accurately and robustly handled without requiring explicit connectivity, outperforming state-of-the-art techniques. The data and code are at https://github.com/yifita/DSS.

differentiable renderer, neural renderer, deep learning
journal: TOGcopyright: rightsretainedjournalyear: 2019journalvolume: 38journalnumber: 6article: 230publicationmonth: 11doi: 10.1145/3355089.3356513
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