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We present an information-theoretic regularization technique for few-shotnovel view synthesis based on neural implicit representation. The proposedapproach minimizes potential reconstruction inconsistency that happens due toinsufficient viewpoints by imposing the entropy constraint of the density ineach ray. In addition, to alleviate the potential degenerate issue when alltraining images are acquired from almost redundant viewpoints, we furtherincorporate the spatially smoothness constraint into the estimated images byrestricting information gains from a pair of rays with slightly differentviewpoints. The main idea of our algorithm is to make reconstructed scenescompact along individual rays and consistent across rays in the neighborhood.The proposed regularizers can be plugged into most of existing neural volumerendering techniques based on NeRF in a straightforward way. Despite itssimplicity, we achieve consistently improved performance compared to existingneural view synthesis methods by large margins on multiple standard benchmarks.

InfoNeRF: Ray Entropy Minimization for Few-Shot Neural Volume Rendering