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

TL;DR

This paper introduces Depth-from-Rotation (DfR), a novel rectification method for uncalibrated rotating cameras that models latitudinal motion to improve stereo rectification and depth estimation.

Contribution

It proposes an analytical rectification approach based on a latitudinal motion assumption, enabling effective depth estimation from uncalibrated rotating cameras.

Findings

Outperforms existing methods in effectiveness and efficiency

Works well on synthetic and real data

Reduces geometric distortion after rectification

Abstract

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.