RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction

Baptiste Brument, Robin Bruneau, Yvain Quéau, Jean Mélou, François Bernard Lauze, Jean-Denis Durou, Lilian Calvet; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 5230-5239

This paper introduces a versatile paradigm for integrating multi-view reflectance (optional) and normal maps acquired through photometric stereo. Our approach employs a pixel-wise joint re-parameterization of reflectance and normal considering them as a vector of radiances rendered under simulated varying illumination. This re-parameterization enables the seamless integration of reflectance and normal maps as input data in neural volume rendering-based 3D reconstruction while preserving a single optimization objective. In contrast recent multi-view photometric stereo (MVPS) methods depend on multiple potentially conflicting objectives. Despite its apparent simplicity our proposed approach outperforms state-of-the-art approaches in MVPS benchmarks across F-score Chamfer distance and mean angular error metrics. Notably it significantly improves the detailed 3D reconstruction of areas with high curvature or low visibility.