MVPSNet: Fast Generalizable Multi-view Photometric Stereo
Dongxu Zhao Daniel Lichy Pierre-Nicolas Perrin Jan-Michael Frahm Soumyadip Sengupta
We propose a fast and generalizable solution to Multi-view Photometric Stereo
(MVPS), called MVPSNet. The key to our approach is a feature extraction network
that effectively combines images from the same view captured under multiple
lighting conditions to extract geometric features from shading cues for stereo
matching. We demonstrate these features, termed `Light Aggregated Feature Maps'
(LAFM), are effective for feature matching even in textureless regions, where
traditional multi-view stereo methods fail. Our method produces similar
reconstruction results to PS-NeRF, a state-of-the-art MVPS method that
optimizes a neural network per-scene, while being 411$\times$ faster (105
seconds vs. 12 hours) in inference. Additionally, we introduce a new synthetic
dataset for MVPS, sMVPS, which is shown to be effective to train a
generalizable MVPS method.