论文标题
深莎多：阴影的神经形状
DeepShadow: Neural Shape from Shadow
论文作者
论文摘要
本文介绍了DeepShadow，这是一种从光度立体声映射中恢复深度图和表面正态的单发方法。试图从光度立体声图像中恢复表面正态的先前作品将铸造阴影视为干扰。我们表明，自我和铸造的阴影不仅不会干扰3D重建，而且可以单独用作强大的学习信号，以恢复深度图和表面正常状态。我们证明，在某些情况下，来自阴影的3D重建甚至可以超越形状。据我们所知，我们的方法是第一个使用神经网络重建3D形状从阴影的方法。该方法不需要任何预训练或昂贵的标记数据，并且在推理期间进行了优化。
This paper presents DeepShadow, a one-shot method for recovering the depth map and surface normals from photometric stereo shadow maps. Previous works that try to recover the surface normals from photometric stereo images treat cast shadows as a disturbance. We show that the self and cast shadows not only do not disturb 3D reconstruction, but can be used alone, as a strong learning signal, to recover the depth map and surface normals. We demonstrate that 3D reconstruction from shadows can even outperform shape-from-shading in certain cases. To the best of our knowledge, our method is the first to reconstruct 3D shape-from-shadows using neural networks. The method does not require any pre-training or expensive labeled data, and is optimized during inference time.
