面向暗光环境下鲁棒且三维感知的RGB-NIR成像

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

July 31, 2026

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

cs.AI

摘要

鲁棒的弱光成像仍然是该领域的一大挑战。近期研究尝试融合近红外（NIR）与含噪RGB以实现更好的增强，然而大多数方法依赖精心整理的训练数据对，在不同场景下的鲁棒性有限。本文引入三维感知神经建模，为RGB-NIR弱光成像提供了全新视角。无需干净RGB监督，即可优化出一个强模型，在三维空间中隐式融合含噪极重的RGB观测与NIR线索，从而有效恢复出清晰的RGB图像。所提模型免除了收集干净RGB数据的需求，并且能在不同噪声水平间泛化。在合成与真实数据上的大量实验证明了其优越性。代码：https://github.com/MyNiuuu/3DarkFusion

English

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