Simple-RF: Regularizing Sparse Input Radiance Fields with Simpler Solutions

Neural Radiance Fields (NeRF) show impressive performances in photo-realistic free-view rendering of scenes. Recent improvements such as TensoRF and ZipNeRF employ explicit models for faster optimization and rendering. However, all these radiance fields require a dense sampling of images in the given scene for effective training. Their performances degrade significantly when only a sparse set of views is available. Existing depth priors used to supervise the radiance fields are either sparse or suffer from generalization issues. We seek to learn scene-specific dense depth priors to regularize the radiance fields. Further, we desire a framework of regularizations that can work across different radiance field models. We observe that certain features of the radiance fields, such as positional encoding, number of decomposed tensor components or size of the hash table, cause overfitting in the sparse-input scenario. We design augmented models by reducing the capacity of these features and train them along with the main radiance field. These augmented models learn simpler solutions, which estimate better depth in certain regions. By supervising the main radiance field with such depths, we significantly improve the performance of the radiance fields on popular forward-facing and 360° datasets by employing the above regularization.