RNb-NeuS: Reflectance and Normal-Based Multi-View 3D Reconstruction
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.
Cite
Text
Brument et al. "RNb-NeuS: Reflectance and Normal-Based Multi-View 3D Reconstruction." Conference on Computer Vision and Pattern Recognition, 2024. doi:10.1109/CVPR52733.2024.00500Markdown
[Brument et al. "RNb-NeuS: Reflectance and Normal-Based Multi-View 3D Reconstruction." Conference on Computer Vision and Pattern Recognition, 2024.](https://mlanthology.org/cvpr/2024/brument2024cvpr-rnbneus/) doi:10.1109/CVPR52733.2024.00500BibTeX
@inproceedings{brument2024cvpr-rnbneus,
title = {{RNb-NeuS: Reflectance and Normal-Based Multi-View 3D Reconstruction}},
author = {Brument, Baptiste and Bruneau, Robin and Quéau, Yvain and Mélou, Jean and Lauze, François Bernard and Durou, Jean-Denis and Calvet, Lilian},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2024},
pages = {5230-5239},
doi = {10.1109/CVPR52733.2024.00500},
url = {https://mlanthology.org/cvpr/2024/brument2024cvpr-rnbneus/}
}