ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision
This work addresses the challenge of 3D shape reconstruction from limited single-view data, which is important for applications like robotics and augmented reality, though it is incremental by building on NeRF with a new supervision technique.
The paper tackles the problem of reconstructing a neural signed distance function (SDF) of a scene from single-view images under multiple lighting conditions by introducing a novel shadow ray supervision scheme, resulting in significant improvements over previous methods on shape reconstruction tasks.
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.