Research map / 3D vision / Depth and stereo
Uncalibrated Photometric Stereo by Stepwise Optimization Using Principal Components of Isotropic BRDFs
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Represent isotropic BRDFs with PCA bases and optimize basis coefficients, normals, and lighting stepwise | Uncalibrated photometric stereo · MERL BRDFs | MAE: 22.8° -> 4.03° | Helped |
| Represent isotropic BRDFs with PCA bases and optimize basis coefficients and normals | Calibrated photometric stereo · MERL BRDFs | MAE: 4.79° -> 1.32° | Helped |
| Increase the number of PCA basis functions stepwise (ablation) | Proposed method · Parametric BRDFs | MAE | Helped |
| Use too few normal clusters in normal grouping (ablation) | Proposed method · Synthetic BRDF experiments | MAE | Hurt |
| Use more normal clusters for normal grouping (ablation) | Proposed method · Synthetic BRDF experiments | MAE | Helped |
| Group similar normals before optimization (ablation) | Proposed method · Synthetic BRDF experiments | convergence | Helped |
| Use randomized multipoint search during optimization (ablation) | Proposed method · Uncalibrated photometric stereo | convergence | Helped |
Closest papers by meaning
- Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces (CVPR 2021 · similarity 0.90)
- A Benchmark Dataset and Evaluation for Non-Lambertian and Uncalibrated Photometric Stereo (CVPR 2016 · similarity 0.90)
- Spin-UP: Spin Light for Natural Light Uncalibrated Photometric Stereo (CVPR 2024 · similarity 0.85)
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- DiLiGenRT: A Photometric Stereo Dataset with Quantified Roughness and Translucency (CVPR 2024 · similarity 0.85)
- RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction (CVPR 2024 · similarity 0.85)
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