Subspace methods for recovering rigid motion I: algorithm and implementation
International Journal of Computer Vision
Geometric computation for machine vision
Geometric computation for machine vision
Performance of optical flow techniques
International Journal of Computer Vision
Linear Differential Algorithm for Motion Recovery: AGeometric Approach
International Journal of Computer Vision
Characterizing Depth Distortion under Different Generic Motions
International Journal of Computer Vision
Understanding the Behavior of SFM Algorithms: A Geometric Approach
International Journal of Computer Vision
Structure from Linear or Planar Motions
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Removal of Translation Bias when Using Subspace Methods
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
From Projective to Euclidean Space Under any Practical Situation, a Criticism of Self-Calibration
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Behaviour of SFM algorithms with erroneous calibration
Computer Vision and Image Understanding
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This article provides an account of sensitivity and robustness of structure and motion recovery with respect to the errors in intrinsic parameters of the camera. We demonstrate both analytically and in simulation, the interplay between measurement and calibration errors and their effect on motion and structure estimates. In particular we show that the calibration errors introduce an additional bias towards the optical axis, which has opposite sign to the bias typically observed by egomotion algorithms. The overall bias causes a distortion of the resulting 3D structure, which we express in a parametric form. The analysis and experiments are carried out in the differential setting for motion and structure estimation from image velocities. While the analytical explanations are derived in the context of linear techniques for motion estimation, we verify our observations experimentally on a variety of optimal and suboptimal motion and structure estimation algorithms. The obtained results illuminate and explain the performance and sensitivity of the differential structure and motion recovery techniques in the presence of calibration errors.