Differentiable Display Photometric Stereo

Abstract

Photometric stereo leverages variations in illumination conditions to reconstruct surface normals. Display photometric stereo which employs a conventional monitor as an illumination source has the potential to overcome limitations often encountered in bulky and difficult-to-use conventional setups. In this paper we present differentiable display photometric stereo (DDPS) addressing an often overlooked challenge in display photometric stereo: the design of display patterns. Departing from using heuristic display patterns DDPS learns the display patterns that yield accurate normal reconstruction for a target system in an end-to-end manner. To this end we propose a differentiable framework that couples basis-illumination image formation with analytic photometric-stereo reconstruction. The differentiable framework facilitates the effective learning of display patterns via auto-differentiation. Also for training supervision we propose to use 3D printing for creating a real-world training dataset enabling accurate reconstruction on the target real-world setup. Finally we exploit that conventional LCD monitors emit polarized light which allows for the optical separation of diffuse and specular reflections when combined with a polarization camera leading to accurate normal reconstruction. Extensive evaluation of DDPS shows improved normal-reconstruction accuracy compared to heuristic patterns and demonstrates compelling properties such as robustness to pattern initialization calibration errors and simplifications in image formation and reconstruction.