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

• URL: http://arxiv.org/abs/2312.01215v2
• Date: Fri, 29 Mar 2024 17:30:58 GMT
• Title: RNb-NeuS: Reflectance and Normal-based Multi-View 3D Reconstruction
• Authors: Baptiste Brument, Robin Bruneau, Yvain Quéau, Jean Mélou, François Bernard Lauze, Jean-Denis, Jean-Denis Durou, Lilian Calvet,
• Abstract summary: This paper introduces a versatile paradigm for integrating multi-view reflectance 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. It significantly improves the detailed 3D reconstruction of areas with high curvature or low visibility.
• Score: 3.1820300989695833
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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