Papers › PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing...
PhySG: Inverse Rendering with Spherical Gaussians for Physics-based Material Editing and Relighting
Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, Noah Snavely
We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images. Our framework represents specular BRDFs and environmental illumination using mixtures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Relighting | Stanford-ORB | PhySG | HDR-PSNR | 21.81 | #7 of 7 | Archive leaderboard | report |
| Image Relighting | Stanford-ORB | PhySG | LPIPS | 0.055 | #7 of 7 | Archive leaderboard | report |
| Image Relighting | Stanford-ORB | PhySG | SSIM | 0.960 | #7 of 7 | Archive leaderboard | report |
| Inverse Rendering | Stanford-ORB | PhySG | HDR-PSNR | 21.81 | #7 of 7 | Archive leaderboard | report |
| Surface Normals Estimation | Stanford-ORB | PhySG | Cosine Distance | 0.17 | #5 of 7 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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