A dynamic programming approach to maximizing tracks for structure from motion

Authors:
Jonathan Mooser;Suya You;Ulrich Neumann;Raphael Grasset;Mark Billinghurst
Affiliations:
CGIT Lab, University of Southern California, Los Angeles, California;CGIT Lab, University of Southern California, Los Angeles, California;CGIT Lab, University of Southern California, Los Angeles, California;HITLabNZ, University of Canterbury, Christchurch, New Zealand;HITLabNZ, University of Canterbury, Christchurch, New Zealand
Venue:
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Year:
2009

Citing 10
Cited 0

MLESAC: a new robust estimator with application to estimating image geometry

Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Automatic Camera Recovery for Closed or Open Image Sequences

ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Multi-camera Scene Reconstruction via Graph Cuts

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Multiple View Geometry in Computer Vision

Multiple View Geometry in Computer Vision
Preemptive RANSAC for Live Structure and Motion Estimation

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Visual Modeling with a Hand-Held Camera

International Journal of Computer Vision
Photo tourism: exploring photo collections in 3D

ACM SIGGRAPH 2006 Papers
Interactive Feature Tracking using K-D Trees and Dynamic Programming

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
An iterative image registration technique with an application to stereo vision

IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present a novel algorithm for improving the accuracy of structure from motion on video sequences. Its goal is to efficiently recover scene structure and camera pose by using dynamic programming to maximize the lengths of putative keypoint tracks. By efficiently discarding poor correspondences while maintaining the largest possible set of inliers, it ultimately provides a robust and accurate scene reconstruction. Traditional outlier detection strategies, such as RANSAC and its derivatives, cannot handle high dimensional problems such as structure from motion over long image sequences. We prove that, given an estimate of the camera pose at a given frame, the outlier detection is optimal and runs in low order polynomial time. The algorithm is applied on-line, processing each frame in sequential order. Results are presented on several indoor and outdoor video sequences processed both with and without the proposed optimization. The improvement in average reprojection errors demonstrates its effectiveness.