Research map / 3D vision / Depth and stereo
Neural Inverse Rendering for General Reflectance Photometric Stereo
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add the specularity channel input to IRNet (ablation) | Neural Inverse Rendering · DiLiGenT | mean angular error | Helped |
| Add global observation blending from PSNet to IRNet (ablation) | Neural Inverse Rendering · DiLiGenT | mean angular error | Helped |
| Use early-stage weak supervision instead of no supervision (ablation) | Neural Inverse Rendering · DiLiGenT | mean angular error | Helped |
| Use early-stage weak supervision instead of all-stage supervision (ablation) | Neural Inverse Rendering · DiLiGenT | mean angular error | Helped |
Closest papers by meaning
- Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces (CVPR 2021 · similarity 0.94)
- UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections (NeurIPS 2024 · similarity 0.88)
- RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction (CVPR 2024 · similarity 0.88)
- GNeRP: Gaussian-guided Neural Reconstruction of Reflective Objects with Noisy Polarization Priors (ICLR 2024 · similarity 0.88)
- Diffusion Reflectance Map: Single-Image Stochastic Inverse Rendering of Illumination and Reflectance (CVPR 2024 · similarity 0.88)
- 3D Reconstruction with Generalizable Neural Fields using Scene Priors (ICLR 2024 · similarity 0.87)
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