# DFR: Depth from Rotation by Uncalibrated Image Rectification with Latitudinal Motion Assumption

arXiv: https://arxiv.org/abs/2307.05129 · HTML: https://mlsanity.com/paper/2307.05129/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 11 Jul 2023.

## Builds on (most cited first, 3 of 3)

- [Structure from Motion for Panorama-Style Videos](https://mlsanity.com/paper/1906.03539/) (2019)
- [Structure from Motion on a Sphere](https://mlsanity.com/paper/1604.00409/) (2016)
- [DSR: Direct Self-rectification for Uncalibrated Dual-lens Cameras](https://mlsanity.com/paper/1809.09763/) (2018)

## Abstract (arXiv)

Despite the increasing prevalence of rotating-style capture (e.g., surveillance cameras), conventional stereo rectification techniques frequently fail due to the rotation-dominant motion and small baseline between views. In this paper, we tackle the challenge of performing stereo rectification for uncalibrated rotating cameras. To that end, we propose Depth-from-Rotation (DfR), a novel image rectification solution that analytically rectifies two images with two-point correspondences and serves for further depth estimation. Specifically, we model the motion of a rotating camera as the camera rotates on a sphere with fixed latitude. The camera's optical axis lies perpendicular to the sphere's surface. We call this latitudinal motion assumption. Then we derive a 2-point analytical solver from directly computing the rectified transformations on the two images. We also present a self-adaptive strategy to reduce the geometric distortion after rectification. Extensive synthetic and real data experiments demonstrate that the proposed method outperforms existing works in effectiveness and efficiency by a significant margin.