Uncalibrated Neural Inverse Rendering for Photometric Stereo of General Surfaces
Abstract
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 groundtruth 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 part 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.