论文标题
RGB-D神经辐射场：用于更快训练的本地抽样
RGB-D Neural Radiance Fields: Local Sampling for Faster Training
论文作者
论文摘要
在计算机视觉中，学习场景的3D表示是一个充满挑战的问题。使用神经辐射场（NERF）的图像中隐式神经表示的最新进展已显示出令人鼓舞的结果。以前基于NERF的方法的某些局限性包括更长的训练时间和不准确的基础几何形状。提出的方法利用RGB-D数据来利用深度感测来改善本地采样来减少训练时间。本文提出了深度引导的本地抽样策略和较小的神经网络体系结构，以实现更快的训练时间而不会损害质量。
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.
