A randomized linear-time algorithm to find minimum spanning trees
Journal of the ACM (JACM)
The Development and Comparison of Robust Methodsfor Estimating the Fundamental Matrix
International Journal of Computer Vision
Multiple view geometry in computer visiond
Multiple view geometry in computer visiond
Introduction to Algorithms
Balanced Recovery of 3D Structure and Camera Motion from Uncalibrated Image Sequences
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
In defence of the 8-point algorithm
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Statistics of shape via principal geodesic analysis on lie groups
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Stable structure from motion for unordered image collections
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
On Camera Calibration with Linear Programming and Loop Constraint Linearization
International Journal of Computer Vision
Rotation averaging with application to camera-rig calibration
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
Enhancing point clouds accuracy of small baseline images based on convex optimization
IEA/AIE'12 Proceedings of the 25th international conference on Industrial Engineering and Other Applications of Applied Intelligent Systems: advanced research in applied artificial intelligence
Simultaneous multiple rotation averaging using lagrangian duality
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
Adaptive structure from motion with a contrario model estimation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
Coupled structure-from-motion and 3D symmetry detection for urban facades
ACM Transactions on Graphics (TOG)
An Adversarial Optimization Approach to Efficient Outlier Removal
Journal of Mathematical Imaging and Vision
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The averaging of multiple pairwise relative motions in a sequence provides a fast and accurate method of camera motion estimation with a wide range of applications, including view registration, robotic path estimation, super-resolution. Since this approach involves averaging in the Lie-algebra of the underlying motion representation, it is non-robust and susceptible to contamination due to outliers in the individual relative motions. In this paper, we introduce a graph-based sampling scheme that efficiently remove such motion outliers. The resulting global motion solution is robust and also provides an empirical estimate of the inherent statistical uncertainty. Example results are provided to demonstrate the efficacy of our approach to incorporating robustness in motion averaging.