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

• URL: http://arxiv.org/abs/2307.05129v1
• Date: Tue, 11 Jul 2023 09:11:22 GMT
• Title: DFR: Depth from Rotation by Uncalibrated Image Rectification with Latitudinal Motion Assumption
• Authors: Yongcong Zhang, Yifei Xue, Ming Liao, Huiqing Zhang, Yizhen Lao
• Abstract summary: We propose Depth-from-Rotation (DfR), a novel image rectification solution for uncalibrated rotating cameras. Specifically, we model the motion of a rotating camera as the camera rotates on a sphere with fixed latitude. We derive a 2-point analytical solver from directly computing the rectified transformations on the two images.
• Score: 6.369764116066747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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