Learning a 3D representation of a scene has been a challenging problem for decades in computer vision. Recent advances in implicit neural representation from images using neural radiance fields(NeRF) have shown promising results. Some of the limitations of previous NeRF based methods include longer training time, and inaccurate underlying geometry. The proposed method takes advantage of RGB-D data to reduce training time by leveraging depth sensing to improve local sampling. This paper proposes a depth-guided local sampling strategy and a smaller neural network architecture to achieve faster training time without compromising quality.

翻译：数十年来,在计算机视野中,学习一个场景的3D代表是一个具有挑战性的问题。最近,使用神经弧度场图像的隐性神经代表方式(NeRF)取得了可喜的成果。以前以NERF为基础的方法的一些局限性包括培训时间更长,以及基础几何不准确。拟议方法利用RGB-D数据,利用深度感测改善当地取样,以减少培训时间。本文提出一个深度引导的地方取样战略和一个较小的神经网络结构,以便在不损害质量的情况下实现更快的培训时间。