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
Computer Vision: A Modern Approach
Computer Vision: A Modern Approach
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
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Edge-Based Methods for Estimating Manhattan Frames in Urban Imagery
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Global Optimization through Rotation Space Search
International Journal of Computer Vision
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multibody structure-and-motion segmentation by branch-and-bound model selection
IEEE Transactions on Image Processing
Fast PRISM: Branch and Bound Hough Transform for Object Class Detection
International Journal of Computer Vision
Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment
International Journal of Robotics Research
An efficient branch-and-bound algorithm for optimal human pose estimation
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Globally optimal line clustering and vanishing point estimation in Manhattan world
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Optimal landmark detection using shape models and branch and bound
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Fast rotation search for real-time interactive point cloud registration
Proceedings of the 18th meeting of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
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A popular approach to detect outliers in a data set is to find the largest consensus set, that is to say maximizing the number of inliers and estimating the underlying model. RANSAC is the most widely used method for this aim but is non-deterministic and does not guarantee to return the optimal solution. In this paper, we consider a rotation model and we present a new approach that performs consensus set maximization in a mathematically guaranteed globally optimal way. We solve the problem by a branch-and-bound framework associated with a rotation space search. Our mathematical formulation can be applied for various computer vision tasks such as panoramic image stitching, 3D registration with a rotating range sensor and line clustering and vanishing point estimation. Experimental results with synthetic and real data sets have successfully confirmed the validity of our approach.