We 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, we develop a simple and effective technique that normalizes the view-dependence by distilling invariant information already encoded in the learned NeRFs. We then jointly train NeRFs for view synthesis with view-dependence normalization to attain quality geometry. Our experiments 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. Our 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. Code is available at: https://github.com/BoifZ/VDN-NeRF.

翻译：我们提出VDN-NeRF方法，用于训练神经辐射场（NeRF），以在非朗伯表面和动态光照条件下获得更优的几何结构。这些条件会导致从不同角度观察时点的辐射度产生显著变化。我们未对引发视角依赖现象的复杂且不具包容性的潜在因素进行显式建模，而是开发了一种简单有效的技术：通过蒸馏已学习NeRF中编码的不变信息来归一化视角依赖性。随后联合训练NeRF进行视角合成，并借助视角依赖性归一化获得高质量几何结构。实验表明，尽管形状-辐射模糊不可避免，但所提出的归一化方法能够最小化其对几何结构的影响，从而本质上对齐解释视角依赖变化所需的最优容量。本方法适用于多种基线模型，且无需改变体渲染管线即可显著提升几何质量，即使数据在移动光源下采集也是如此。代码已开源：https://github.com/BoifZ/VDN-NeRF。