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 https://github.com/gerwang/ShadowNeuS.

翻译：通过监督场景与多视角图像平面之间的相机光线，NeRF实现了面向新视图合成任务的神经场景表示重建。然而，光源与场景之间的阴影光线尚未得到充分利用。为此，我们提出一种新型阴影光线监督方案，同时优化沿光线分布的采样点及光线位置。通过监督阴影光线，我们成功从多光照条件下的单视角图像重建出场景的神经符号距离场。利用单视角二值阴影，我们训练神经网络重建不受相机视线限制的完整场景。通过进一步建模图像颜色与阴影光线之间的相关性，本方法可有效扩展至RGB输入。我们将该方法与现有工作在单视角二值阴影或RGB图像的三维形状重建任务上进行对比，观察到显著性能提升。代码与数据见https://github.com/gerwang/ShadowNeuS。