In this project, we consider the problem of estimating surface normals of a scene with spatially varying, general BRDFs observed by a static camera under varying, known, distant illumination. Unlike previous approaches that are mostly based on continuous local optimization, we cast the problem as a discrete hypothesis-and-test search problem over the discretized space of surface normals. In this manner, our method searches for the globally optimal surface normal from all (discretized) possible ones.

Publication

• • Kenji Enomoto and Michael Waechter and Kiriakos N. Kutulakos and Yasuyuki Matsushita: Photometric Stereo via Discrete Hypothesis-and-Test Search, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020. [paper]