Neural radiance fields, or NeRF, represent a breakthrough in the field of novel view synthesis and 3D modeling of complex scenes from multi-view image collections. Numerous recent works have shown the importance of making NeRF models more robust, by means of regularization, in order to train with possibly inconsistent and/or very sparse data. In this work, we explore how differential geometry can provide elegant regularization tools for robustly training NeRF-like models, which are modified so as to represent continuous and infinitely differentiable functions. In particular, we present a generic framework for regularizing different types of NeRFs observations to improve the performance in challenging conditions. We also show how the same formalism can also be used to natively encourage the regularity of surfaces by means of Gaussian or mean curvatures.
翻译:神经光亮场(NeRF)代表了从多视图图像收藏中对复杂场景进行新颖观点合成和3D建模领域的突破。许多最近的工作表明,通过正规化,使NeRF模型更加稳健,以培训可能不一致和(或)非常稀少的数据。在这项工作中,我们探讨了不同的几何方法如何为强力培训类似NeRF的模型提供优雅的正规化工具,这些模型经过修改,以代表连续和无限不同的功能。特别是,我们提出了一个将不同类型的NERF观测正规化的通用框架,以改善具有挑战性的条件下的性能。我们还展示了如何利用同样的形式主义,通过高斯或中度弯曲法,本地鼓励表面的正常化。