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Research map / 3D vision / Depth and stereo

Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces

CVPR 2021 · 3D vision › Depth and stereo · approach: Photometric stereo · 50 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use image reconstruction with explicit interreflection modeling instead of ground-truth normal supervisionOurs · DiLiGenT averageMAE (degrees): 11.62; Chen et al. (2019): 9.51Mixed
Use image reconstruction with explicit interreflection modeling instead of ground-truth normal supervisionOurs · Proposed dataset averageMAE: 14.74; Nayar et al. (1991): 18.17Helped
Use estimated rather than ground-truth light directions and intensitiesOurs · Proposed dataset averageMAE: 12.68 -> 14.74; +2.06Hurt
Replace classical initialization with robust partial-sum-of-singular-values initialization (ablation)Ours · CowMAEHelped
Remove weak-supervision loss during early optimization (ablation)Ours · CowMAEHurt
Remove interreflection modeling and render using the initial normal map (ablation)Ours · VaseMAEHurt
Increase Gaussian image noiseOurs · Proposed dataset, noisy imagesMAEHurt
Apply the method to a surface with deep concavities and large discontinuitiesOurs · RoseMAE: 60.82 degreesHurt

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