By supervising camera rays between a scene and multi-view image planes, NeRF
reconstructs a neural scene representation for the task of novel view
synthesis. On the other hand, shadow rays between the light source and the
scene have yet to be considered. Therefore, we propose a novel shadow ray
supervision scheme that optimizes both the samples along the ray and the ray
location. By supervising shadow rays, we successfully reconstruct a neural SDF
of the scene from single-view images under multiple lighting conditions. Given
single-view binary shadows, we train a neural network to reconstruct a complete
scene not limited by the camera's line of sight. By further modeling the
correlation between the image colors and the shadow rays, our technique can
also be effectively extended to RGB inputs. We compare our method with previous
works on challenging tasks of shape reconstruction from single-view binary
shadow or RGB images and observe significant improvements. The code and data
are available at this https URL

本文提出了一种用于 novel view synthesis 和 shape reconstruction 的新方法 NeRF，并通过在场景和多视图图像平面之间监督相机光线以及在光源和场景之间监督阴影光线的方式不断优化整个场景的神经表征。