Roll2Depth: Zero-Shot Metric Depth Estimation Exploiting Camera's Rolling Shutter Effect

Accurate monocular metric depth estimation, i.e., predicting absolute depth values, is critical for applications such as robotics and augmented reality. However, existing methods often suffer from poor transferability to in-the-wild scenarios due to the lack of knowledge of camera intrinsics. In this paper, we propose a novel framework, Roll2Depth, to recover metric scale in unseen environments with unknown camera parameters. Our key idea is to leverage scene-independent physical cues induced by the rolling shutter effect of standard CMOS sensors as metric priors. When a scene is illuminated by a flickering light, the rolling shutter produces distinctive stripe-like spatial patterns, revealing the scene's shading with and without the flickering light source. This contrast encodes absolute depth cues that can be exploited for metric estimation. Roll2Depth isolates these patterns as metric priors to guide affine-invariant models on metric depth estimation in a zero-shot setting. Our evaluations on standard benchmark datasets, as well as a real-world dataset collected using commercial smartphones and our prototype system, demonstrate that Roll2Depth, as a low-cost active solution, outperforms state-of-the-art passive baselines in zero-shot settings. These results highlight its potential impact in applications such as indoor robotics and embodied perception, where affordable and effective active sensing would be valuable.