Fast and Accurate Algorithms for Projective Multi-Image Structure from Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computing Optical Flow with Physical Models of Brightness Variation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal instantaneous rigid motion estimation insensitive to local minima
Computer Vision and Image Understanding
On the Consistency of the Normalized Eight-Point Algorithm
Journal of Mathematical Imaging and Vision
Motion bias and structure distortion induced by intrinsic calibration errors
Image and Vision Computing
An Efficient Linear Method for the Estimation of Ego-Motion from Optical Flow
Proceedings of the 31st DAGM Symposium on Pattern Recognition
A review and evaluation of methods estimating ego-motion
Computer Vision and Image Understanding
Quasi-Parallax for Nearly Parallel Frontal Eyes
International Journal of Computer Vision
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Given estimates of the motion field (optic flow) from an image sequence, it is possible to recover translational direction, ~ T , using a variety of techniques. One such technique, known as "subspace methods," generates constraints which are perpendicular to ~ T , so that two distinct constraints allow a solution for ~ T . In practice many constraints are used in a least-squares solution, but it has been observed that the recovered estimates for ~ T are biased towards the optical axis. While the cause of the bias is well known, previous attempts to remove it have been awed. This paper outlines a new method which removes the bias. The technique is simple to apply and computationally efficient.