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By supervising camera rays between a scene and multi-view image planes, NeRFreconstructs a neural scene representation for the task of novel viewsynthesis. On the other hand, shadow rays between the light source and thescene have yet to be considered. Therefore, we propose a novel shadow raysupervision scheme that optimizes both the samples along the ray and the raylocation. By supervising shadow rays, we successfully reconstruct a neural SDFof the scene from single-view images under multiple lighting conditions. Givensingle-view binary shadows, we train a neural network to reconstruct a completescene not limited by the camera's line of sight. By further modeling thecorrelation between the image colors and the shadow rays, our technique canalso be effectively extended to RGB inputs. We compare our method with previousworks on challenging tasks of shape reconstruction from single-view binaryshadow or RGB images and observe significant improvements. The code and dataare available at https://github.com/gerwang/ShadowNeuS.

ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision