DFR: Depth from Rotation by Uncalibrated Image Rectification with
  Latitudinal Motion Assumption
Yongcong Zhang Yifei Xue Ming Liao Huiqing Zhang Yizhen Lao
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.