论文标题
未校准的神经反向渲染，用于一般表面的光度立体声
Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
论文作者
论文摘要
本文提出了一个未校准的深度神经网络框架，用于光度法立体声问题。为了解决问题，现有的基于神经网络的方法需要精确的光方向或物体的地面正态或两者兼而有之。但是，在实践中，精确地购买这两种信息是一项挑战，这限制了对视觉应用的更广泛采用光度法立体声算法。为了绕过这一困难，我们提出了针对此问题的未校准神经反向渲染方法。我们的方法首先从输入图像估算光方向，然后优化图像重建损失以计算表面符号，双向反射率分布函数值和深度。此外，我们的公式明确对复杂表面的凹形和凸成分进行了建模，以考虑图像形成过程中反射的影响。对具有挑战性的主题对拟议方法的广泛评估通常比监督和经典方法表现出可比或更好的结果。
This paper presents an uncalibrated deep neural network framework for the photometric stereo problem. For training models to solve the problem, existing neural network-based methods either require exact light directions or ground-truth surface normals of the object or both. However, in practice, it is challenging to procure both of this information precisely, which restricts the broader adoption of photometric stereo algorithms for vision application. To bypass this difficulty, we propose an uncalibrated neural inverse rendering approach to this problem. Our method first estimates the light directions from the input images and then optimizes an image reconstruction loss to calculate the surface normals, bidirectional reflectance distribution function value, and depth. Additionally, our formulation explicitly models the concave and convex parts of a complex surface to consider the effects of interreflections in the image formation process. Extensive evaluation of the proposed method on the challenging subjects generally shows comparable or better results than the supervised and classical approaches.
