Camera Calibration by Vanishing Lines for 3-D Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape and motion from image streams under orthography: a factorization method
International Journal of Computer Vision
A simple, intuitive camera calibration tool for natural images
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
Error characterization of the factorization method
Computer Vision and Image Understanding
A Factorization Based Algorithm for Multi-Image Projective Structure and Motion
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume II - Volume II
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Structure from Planar Motions with Small Baselines
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Structure from Linear or Planar Motions
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Motion and Shape Recovery Based on Iterative Stabilization for Modest Deviation from Planar Motion
IEEE Transactions on Pattern Analysis and Machine Intelligence
5D motion subspaces for planar motions
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
3D face and motion estimation from sparse points using adaptive bracketed minimization
Multimedia Tools and Applications
Multilinear Factorizations for Multi-Camera Rigid Structure from Motion Problems
International Journal of Computer Vision
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We propose a factorization method for structure from planar motion using a stationary perspective camera. Compared with [A factorization based algorithm for multi-image projective structure and motion] forgeneral motion, our work has three major differences: a different measurement matrix specialized for planar motion is formed. The measurement matrix has a rank of at most 3, instead of 4; the measurement matrix needs similar scalings, but estimation of fundamental matrices or epipoles is not needed; we have an Euclidean reconstruction instead of a projective reconstruction. The camera is not required to be calibrated. A simple semi-automatic calibration method using vanishing points and lines is sufficient. Experimental results show that the algorithm is accurate and fairly robust to noise and inaccurate calibration.