MVPSNet: Fast Generalizable Multi-view Photometric Stereo

Abstract

We propose a fast and generalizable solution to Multiview 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 often 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× faster (105 seconds vs. 12 hours) in inference. Additionally, we introduce a new synthetic dataset for MVPS, sMVPS, which is shown to be effective for training a generalizable MVPS method.