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 ∼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.
@article{arxiv.2603.05330,
title = {Dark3R: Learning Structure from Motion in the Dark},
author = {Andrew Y Guo and Anagh Malik and SaiKiran Tedla and Yutong Dai and Yiqian Qin and Zach Salehe and Benjamin Attal and Sotiris Nousias and Kiriakos N. Kutulakos and David B. Lindell},
journal= {arXiv preprint arXiv:2603.05330},
year = {2026}
}