Neural Radiance Fields (NeRF) show impressive performance for the photorealistic 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

翻译：神经辐射场（NeRF）在场景的逼真自由视角渲染中展现出卓越性能。然而，NeRF需要给定场景中图像的密集采样，当仅提供稀疏视图集时，其性能会显著下降。研究者发现，监督NeRF估计的深度有助于在较少视图下有效训练模型。深度监督要么通过经典方法获得，要么借助在大型数据集上预训练的神经网络获得。前者可能仅提供稀疏监督，而后者可能面临泛化问题。与以往方法不同，我们通过设计增强模型并与NeRF共同训练来学习深度监督。我们通过探索位置编码和视角相关辐射在少样本NeRF训练中的作用，设计了鼓励简化解的增强模型。这些简化模型估计的深度用于监督NeRF的深度估计。由于增强模型在某些区域可能不准确，我们设计了一种机制，仅选择可靠的深度估计进行监督。最后，我们在NeRF的粗粒和细粒多层感知器之间添加一致性损失，以确保更好地利用层次化采样。通过应用上述正则化方法，我们在两个流行数据集上实现了最先进的视图合成性能。模型的源代码可在我们的项目页面找到：https://nagabhushansn95.github.io/publications/2023/SimpleNeRF.html