Get 3D shape even with changing light using view-dependence normalization with VDN-NeRF
Get 3D shape even with changing light using view-dependence normalization with VDN-NeRF
VDN-NeRF: Resolving Shape-Radiance Ambiguity via View-Dependence Normalization
arXiv paper abstract https://arxiv.org/abs/2303.17968
arXiv PDF paper https://arxiv.org/pdf/2303.17968.pdf
... propose VDN-NeRF, a method to train neural radiance fields (NeRFs) for better geometry under non-Lambertian surface and dynamic lighting conditions that cause significant variation in the radiance of a point when viewed from different angles.
Instead of explicitly modeling the underlying factors that result in the view-dependent phenomenon, which could be complex yet not inclusive, ... develop a simple and effective technique that normalizes the view-dependence by distilling invariant information already encoded in the learned NeRFs.
... then jointly train NeRFs for view synthesis with view-dependence normalization to attain quality geometry.
... show that even though shape-radiance ambiguity is inevitable, the proposed normalization can minimize its effect on geometry, which essentially aligns the optimal capacity needed for explaining view-dependent variations.
... method applies to various baselines and significantly improves geometry without changing the volume rendering pipeline, even if the data is captured under a moving light source ...
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