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 360ˆ datasets by employing the above regularization.

Neural Radiance Fields (NeRF) show impressive performance for the photo-realistic free-view rendering of scenes. However, NeRFs require dense sampling of images in the given scene, and their performance degrades significantly when only a sparse set of views are available. Researchers have found that supervising the depth estimated by the NeRF helps train it effectively with fewer views. The depth supervision is obtained either using classical approaches or neural networks pre-trained on a large dataset. While the former may provide only sparse supervision, the latter may suffer from generalization issues. As opposed to the earlier approaches, we seek to learn the depth supervision by designing augmented models and training them along with the NeRF. We design augmented models that encourage simpler solutions by exploring the role of positional encoding and view-dependent radiance in training the few-shot NeRF. The depth estimated by these simpler models is used to supervise the NeRF depth estimates. Since the augmented models can be inaccurate in certain regions, we design a mechanism to choose only reliable depth estimates for supervision. Finally, we add a consistency loss between the coarse and fine multi-layer perceptrons of the NeRF to ensure better utilization of hierarchical sampling. We achieve state-of-the-art view-synthesis performance on two popular datasets by employing the above regularizations. The source code for our model can be found on our project page: https://nagabhushansn95.github.io/publications/2023/SimpleNeRF.html