  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.
