Differentiable Surface Splatting for Point-based Geometry Processing
About
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.
Wang Yifan, Felice Serena, Shihao Wu, Cengiz \"Oztireli, Olga Sorkine-Hornung• 2019
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Multi-view 3D Reconstruction | Dragon (test) | Symmetric Hausdorff Distance0.1795 | 3 | |
| Multi-view 3D Reconstruction | Torus (test) | Symmetric Hausdorff Distance0.0354 | 2 | |
| Multi-view 3D Reconstruction | Bunny (test) | Symmetric Hausdorff Distance0.1094 | 2 |
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