Papers › Neural-PBIR Reconstruction of Shape, Material, and Illumination
Neural-PBIR Reconstruction of Shape, Material, and Illumination
Cheng Sun, Guangyan Cai, Zhengqin Li, Kai Yan, Cheng Zhang, Carl Marshall, Jia-Bin Huang, Shuang Zhao, Zhao Dong
Reconstructing the shape and spatially varying surface appearances of a physical-world object as well as its surrounding illumination based on 2D images (e.g., photographs) of the object has been a long-standing problem in computer vision and graphics. In this paper, we introduce an accurate and highly efficient object reconstruction pipeline combining neural based object reconstruction and physics-based inverse rendering (PBIR). Our pipeline firstly leverages a neural SDF based shape reconstruction to produce high-quality but potentially imperfect object shape. Then, we introduce a neural material and lighting distillation stage to achieve high-quality predictions for material and illumination. In the last stage, initialized by the neural predictions, we perform PBIR to refine the initial results and obtain the final high-quality reconstruction of object shape, material, and illumination. Experimental results demonstrate our pipeline significantly outperforms existing methods quality-wise and performance-wise.
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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 | Neural-PBIR | HDR-PSNR | 26.01 | #1 of 7 | Archive leaderboard | report |
| Image Relighting | Stanford-ORB | Neural-PBIR | LPIPS | 0.023 | #1 of 7 | Archive leaderboard | report |
| Image Relighting | Stanford-ORB | Neural-PBIR | SSIM | 0.979 | #1 of 7 | Archive leaderboard | report |
| Inverse Rendering | Stanford-ORB | Neural-PBIR | HDR-PSNR | 26.01 | #1 of 7 | Archive leaderboard | report |
| Surface Normals Estimation | Stanford-ORB | Neural-PBIR | Cosine Distance | 0.06 | #4 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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