论文标题
Shadowneus：阴影射线监督的神经SDF重建
ShadowNeuS: Neural SDF Reconstruction by Shadow Ray Supervision
论文作者
论文摘要
通过监督场景和多视图图像平面之间的相机光线，NERF重建了新视图合成任务的神经场景表示。另一方面，光源和场景之间的阴影射线尚未考虑。因此，我们提出了一种新颖的影子射线监督方案，该方案优化了沿射线和射线位置的样品。通过监督影子射线，我们成功地从单个照明条件下的单视图像中成功重建了场景的神经SDF。给定单视二进制阴影，我们训练一个神经网络，重建一个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，我们的技术也可以有效地扩展到RGB输入。我们将我们的方法与以前有关从单视二进制二进制阴影或RGB图像进行挑战的形状重建任务的工作进行了比较，并观察到了重大改进。代码和数据可在https://github.com/gerwang/shadowneus上找到。
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.
