在极低信噪比下实现3D结构重建，无需3D标注
Dark3R: Learning Structure from Motion in the Dark
- 用教师-学生蒸馏法适配大模型到暗光环境
- 在-4 dB以下信噪比下仍能准确估计相机位姿
- 适合夜间视觉、弱光成像等极端场景应用
我们提出Dark3R，一种在暗光条件下进行结构从运动（SfM）的框架，直接处理信噪比低于-4 dB的原始图像——这是传统特征与学习方法失效的区域。核心思想是通过教师-学生知识蒸馏，将大规模3D基础模型适配至极端低光环境，从而实现鲁棒的特征匹配与相机位姿估计。Dark3R无需3D监督，仅使用噪声-清晰原始图像对训练，这些图像可直接拍摄或通过简单泊松-高斯噪声模型施加于曝光良好的原始图像生成。为训练与评估，我们构建了一个新的曝光分级数据集，包含约42,000张多视角原始图像及真实3D标注。实验表明，Dark3R在低信噪比条件下达到当前最优的结构从运动性能；进一步结合粗到精辐射场优化，实现了暗光环境下的先进新视角合成。
原文摘要 · Abstract (English)
We introduce Dark3R, a framework for structure from motion in the dark that operates directly on raw images with signal-to-noise ratios (SNRs) below $-4$ dB -- a regime where conventional feature- and learning-based methods break down. Our key insight is to adapt large-scale 3D foundation models to extreme low-light conditions through a teacher--student distillation process, enabling robust feature matching and camera pose estimation in low light. Dark3R requires no 3D supervision; it is trained solely on noisy--clean raw image pairs, which can be either captured directly or synthesized using a simple Poisson--Gaussian noise model applied to well-exposed raw images. To train and evaluate our approach, we introduce a new, exposure-bracketed dataset that includes $\sim$42,000 multi-view raw images with ground-truth 3D annotations, and we demonstrate that Dark3R achieves state-of-the-art structure from motion in the low-SNR regime. Further, we demonstrate state-of-the-art novel view synthesis in the dark using Dark3R's predicted poses and a coarse-to-fine radiance field optimization procedure.
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