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

Baptiste Brument · Robin Bruneau · Yvain Queau · Jean Mélou · Francois Lauze · Jean-Denis Durou · Lilian Calvet

Arch 4A-E Poster #38
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Wed 19 Jun 5 p.m. PDT — 6:30 p.m. PDT


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. 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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