从低光原始图像中重建高质量3D场景，同时修复颜色失真和降噪。
Bright-NeRF:Brightening Neural Radiance Field with Color Restoration from Low-light Raw Images
- 基于传感器响应物理模型，无监督学习增强辐射场。
- 在低光下实现色温自适应，显著提升合成视图质量。
- 适用于夜间或弱光环境的3D内容生成，适合计算机视觉研究者。
神经辐射场（NeRF）在新视角合成任务中表现优异，但其输入依赖正常光照条件下的图像采集，难以在低光环境下准确建模场景——此时图像常伴随严重噪声与色彩失真。为此，本文提出Bright-NeRF，一种从多视角低光原始图像中无监督学习高质量辐射场的新方法，可同步实现颜色恢复、去噪与增强的新视角合成。我们引入基于物理的传感器响应模型，并设计色适应损失以约束响应学习，确保不同光照条件下物体颜色感知的一致性；同时利用原始数据特性自动提取场景亮度信息。此外，我们构建了一个多视角低光原始图像数据集，推动该领域研究进展。实验表明，本方法显著优于现有2D与3D基线方法。代码与数据集将公开发布。
原文摘要 · Abstract (English)
Neural Radiance Fields (NeRFs) have demonstrated prominent performance in novel view synthesis. However, their input heavily relies on image acquisition under normal light conditions, making it challenging to learn accurate scene representation in low-light environments where images typically exhibit significant noise and severe color distortion. To address these challenges, we propose a novel approach, Bright-NeRF, which learns enhanced and high-quality radiance fields from multi-view low-light raw images in an unsupervised manner. Our method simultaneously achieves color restoration, denoising, and enhanced novel view synthesis. Specifically, we leverage a physically-inspired model of the sensor's response to illumination and introduce a chromatic adaptation loss to constrain the learning of response, enabling consistent color perception of objects regardless of lighting conditions. We further utilize the raw data's properties to expose the scene's intensity automatically. Additionally, we have collected a multi-view low-light raw image dataset to advance research in this field. Experimental results demonstrate that our proposed method significantly outperforms existing 2D and 3D approaches. Our code and dataset will be made publicly available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。