论文标题
NEIF：代表一般反射率是未校准光度立体声的神经内在字段
NeIF: Representing General Reflectance as Neural Intrinsics Fields for Uncalibrated Photometric Stereo
论文作者
论文摘要
由于未知光带来的固有歧义，未校准的光度立体声（UPS）具有挑战性。现有的解决方案通过将反射率明确关联到光条件或以监督方式解决光条件来减轻歧义。本文建立了光线线索和光估计之间的隐式关系，并以无监督的方式解决了UPS。关键思想是将反射率表示为四个神经内在字段，即位置，光，镜头和阴影，基于神经光场与镜面反射和铸造阴影的光线线索隐式相关。神经内在字段的无监督，关节优化可以不受训练数据偏差和累积误差，并完全利用所有观察到的像素值的UPS值。我们的方法在常规且具有挑战性的设置下，在公共和自我收集的数据集上获得了优于最先进的UPS方法。该代码将很快发布。
Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by unknown light. Existing solutions alleviate the ambiguity by either explicitly associating reflectance to light conditions or resolving light conditions in a supervised manner. This paper establishes an implicit relation between light clues and light estimation and solves UPS in an unsupervised manner. The key idea is to represent the reflectance as four neural intrinsics fields, i.e., position, light, specular, and shadow, based on which the neural light field is implicitly associated with light clues of specular reflectance and cast shadow. The unsupervised, joint optimization of neural intrinsics fields can be free from training data bias as well as accumulating error, and fully exploits all observed pixel values for UPS. Our method achieves a superior performance advantage over state-of-the-art UPS methods on public and self-collected datasets, under regular and challenging setups. The code will be released soon.
