Toward Robust and 3D-Aware RGB-NIR Imaging in the Dark

Muyao Niu; Mingze Ma; Yifan Zhan; Qingtian Zhu; Zhihang Zhong; Wei Guo; Chang Wen Chen; Yinqiang Zheng

Toward Robust and 3D-Aware RGB-NIR Imaging in the Dark

Computer Vision and Pattern Recognition 2026-07-31 v1

Authors: Muyao Niu , Mingze Ma , Yifan Zhan , Qingtian Zhu , Zhihang Zhong , Wei Guo , Chang Wen Chen , Yinqiang Zheng

Abstract

Robust low-light imaging remains challenging for the community. Recent studies have explored fusing Near-Infrared (NIR) with noisy RGB to achieve improved enhancement, yet most methods depend on carefully curated training data pairs, with limited robustness under different scenarios. This paper offers a new perspective for RGB-NIR low-light imaging by incorporating 3D-aware neural modeling. Without using clean RGB supervision, a powerful model can be optimized to implicitly fuse extremely noisy RGB observations with NIR cues in 3D space, effectively recovering clean RGB images. The proposed model obviates the requirement for clean RGB data collection, generalizes across different noise levels. Extensive evaluations on synthetic and real data demonstrate its superiority. Codes available: https://github.com/MyNiuuu/3DarkFusion

Keywords

low-light imaging RGB-NIR fusion 3D-aware modeling neural rendering image restoration

Cite

@article{arxiv.2607.29684,
  title  = {Toward Robust and 3D-Aware RGB-NIR Imaging in the Dark},
  author = {Muyao Niu and Mingze Ma and Yifan Zhan and Qingtian Zhu and Zhihang Zhong and Wei Guo and Chang Wen Chen and Yinqiang Zheng},
  journal= {arXiv preprint arXiv:2607.29684},
  year   = {2026}
}

Comments

ACM Multimedia 2026, Codes and Models: https://github.com/MyNiuuu/3DarkFusion