论文标题
deepps2：使用两个不同照明图像重新访问光度法立体声
DeepPS2: Revisiting Photometric Stereo Using Two Differently Illuminated Images
论文作者
论文摘要
光度立体声是使用在不同照明下捕获的物体的图像恢复3D表面正态的问题，在计算机视觉研究中具有极大的兴趣和重要性。尽管现有的传统和深度学习方法取得了成功，但由于：（i）三个或更多不同的照明图像的要求仍然具有挑战性，（ii）无法对未知的一般反射率进行建模，以及（iii）准确的3D地面真实地面正常型和已知的培训信息的要求。在这项工作中，我们尝试使用仅两个不同照明的图像，称为PS2问题，解决了一个未经探索的光度立体声问题。这是一种基于单个图像的重建方法（例如Shape（SFS）的形状）和传统的光度立体声（PS）之间的中间情况，该方法需要三个或更多图像。我们提出了一个基于反向渲染的深度学习框架，称为DEEPPS2，该框架共同执行表面正常，反照率，照明估算和图像重新估算，并以完全自我监督的方式重新确定，而无需地面真相数据。我们演示了与图像重建结合结合的图像重新构造如何在自我监督的设置中增强照明估计。
Photometric stereo, a problem of recovering 3D surface normals using images of an object captured under different lightings, has been of great interest and importance in computer vision research. Despite the success of existing traditional and deep learning-based methods, it is still challenging due to: (i) the requirement of three or more differently illuminated images, (ii) the inability to model unknown general reflectance, and (iii) the requirement of accurate 3D ground truth surface normals and known lighting information for training. In this work, we attempt to address an under-explored problem of photometric stereo using just two differently illuminated images, referred to as the PS2 problem. It is an intermediate case between a single image-based reconstruction method like Shape from Shading (SfS) and the traditional Photometric Stereo (PS), which requires three or more images. We propose an inverse rendering-based deep learning framework, called DeepPS2, that jointly performs surface normal, albedo, lighting estimation, and image relighting in a completely self-supervised manner with no requirement of ground truth data. We demonstrate how image relighting in conjunction with image reconstruction enhances the lighting estimation in a self-supervised setting.
