MVCPS-NeuS: Multi-View Constrained Photometric Stereo for Neural Surface Reconstruction

Multi-view photometric stereo (MVPS) recovers a high-fidelity 3D shape of a scene by benefiting from both multi-view stereo and photometric stereo. While photometric stereo boosts detailed shape reconstruction, it necessitates recording images under various light conditions for each viewpoint. In particular, calibrating the light directions for each view significantly increases the cost of acquiring images. To make MVPS more accessible, we introduce a practical and easy-to-implement setup, multi-view constrained photometric stereo (MVCPS), where the light directions are unknown but constrained to move together with the camera. Unlike con-ventional multi-view uncalibrated photometric stereo, our constrained setting reduces the ambiguities of surface normal estimates from per-view linear ambiguities to a single and global linear one, thereby simplifying the disambiguation process. The proposed method integrates the ambiguous surface normal into neural surface reconstruction (NeuS) to simultaneously resolve the global ambiguity and estimate the detailed 3D shape. Experiments demonstrate that our method estimates accurate shapes under sparse viewpoints using only a few multi-view constrained light sources.