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 pure shadow or RGB 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 will be released.

翻译：NERF通过监督场景和多视图像平面之间的摄像射线,重建了用于新视觉合成任务的神经场景演示。 另一方面,光源和场景之间的影子光线还有待考虑。 因此,我们提出一个新的影子光线监督计划,在光线和光线位置上优化样本。 通过监督影子光线,我们成功地在多个照明条件下从单一视觉纯影或RGB图像中重建了场景的神经SDF。 在一眼双影的阴影下,我们训练了一个神经网络,以重建一个不受摄像头视线限制的完整场景。通过进一步建模图像颜色和影子光线之间的联系,我们的技术也可以有效地扩展至RGB投入。我们将我们的方法与从单视双影或RGB图像中挑战形状重建任务的工作进行比较,并观察重大改进。代码和数据将会被发布。