Papers › NeRD: Neural Reflectance Decomposition from Image Collections
NeRD: Neural Reflectance Decomposition from Image Collections
Mark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron, Ce Liu, Hendrik P. A. Lensch
Decomposing a scene into its shape, reflectance, and illumination is a challenging but important problem in computer vision and graphics. This problem is inherently more challenging when the illumination is not a single light source under laboratory conditions but is instead an unconstrained environmental illumination. Though recent work has shown that implicit representations can be used to model the radiance field of an object, most of these techniques only enable view synthesis and not relighting. Additionally, evaluating these radiance fields is resource and time-intensive. We propose a neural reflectance decomposition (NeRD) technique that uses physically-based rendering to decompose the scene into spatially varying BRDF material properties. In contrast to existing techniques, our input images can be captured under different illumination conditions. In addition, we also propose techniques to convert the learned reflectance volume into a relightable textured mesh enabling fast real-time rendering with novel illuminations. We demonstrate the potential of the proposed approach with experiments on both synthetic and real datasets, where we are able to obtain high-quality relightable 3D assets from image collections. The datasets and code is available on the project page: https://markboss.me/publication/2021-nerd/
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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 | NeRD | HDR-PSNR | 23.29 | #5 of 7 | Archive leaderboard | report |
| Image Relighting | Stanford-ORB | NeRD | LPIPS | 0.059 | #5 of 7 | Archive leaderboard | report |
| Image Relighting | Stanford-ORB | NeRD | SSIM | 0.957 | #5 of 7 | Archive leaderboard | report |
| Inverse Rendering | Stanford-ORB | NeRD | HDR-PSNR | 23.29 | #5 of 7 | Archive leaderboard | report |
| Surface Normals Estimation | Stanford-ORB | NeRD | Cosine Distance | 0.28 | #6 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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