Does Physical Interpretability of Observation Map Improve Photometric Stereo Networks?

In this paper, we revisit observation map which is the input representation for the deep photometric stereo networks where pixelwise observations under different lights are protectively integrated to handle an arbitrary number of input images. Based on the hypothesis that the physical interpretability of observation map contributes to its performance, we empirically validate it by proposing two novel ideas; one is a pixelwise unified inverse rendering framework which accounts the physical reasoning to recover the surface normals and the other is the network architecture that is equivariant/invariant to the view-axis-around rotation of the pixelwise observation map. By introducing these two ideas, our experimental evaluation on the public dataset indicated that more explicit physical reasoning of observation map improves the performance of the photometric stereo task.