Given a video sequence acquired by an uncalibrated camera rotating about a fixed center of projection, it is desired to estimate the appearance of the scene in all possible directions, recover the camera orientations, and determine the constant focal length and radial lens distortion of the camera, without recourse to physical scene measurements. While parts of this problem have been studied before, there has not yet been a comprehensive algorithmic solution that accommodates a variety of trajectories nor a characterization of the prerequisites for successful reconstruction. This paper studies a computationally efficient algorithm that takes maximum advantage of all information available locally among pairs or small groups of frames as well as global closed cycle constraints. It works with any rotational trajectory, not just panning about a fixed axis. Analyzing the algorithm's error surface predicts the sensitivity of estimation to lens distortion and global constraints. Both the analysis and experiments with natural images demonstrate that it is crucial to use either lens distortion compensation or closed cycle constraints to get accurate parameter estimates and well-aligned mosaic images.
['Stephen Hsu', 'Harpreet S. Sawhney']
Influence of global constraints and lens distortion on pose and appearance recovery from a purely rotating camera