Robust regression and outlier detection
Robust regression and outlier detection
Robust Parameter Estimation in Computer Vision
SIAM Review
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
Matching with PROSAC " Progressive Sample Consensus
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Guided-MLESAC: Faster Image Transform Estimation by Using Matching Priors
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomized RANSAC with Sequential Probability Ratio Test
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Multiple Structures Estimation with J-Linkage
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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
Energy-Based Geometric Multi-model Fitting
International Journal of Computer Vision
Interactive object modelling based on piecewise planar surface patches
Computer Vision and Image Understanding
A simultaneous sample-and-filter strategy for robust multi-structure model fitting
Computer Vision and Image Understanding
Sampling Minimal Subsets with Large Spans for Robust Estimation
International Journal of Computer Vision
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Random hypothesis generation underpins many geometric model fitting techniques. Unfortunately it is also computationally expensive. We propose a fundamentally new approach to accelerate hypothesis sampling by guiding it with information derived from residual sorting. We show that residual sorting innately encodes the probability of two points to have arisen from the same model and is obtained without recourse to domain knowledge (e.g. keypoint matching scores) typically used in previous sampling enhancement methods. More crucially our approach is naturally capable of handling data with multiple model instances and excels in applications (e.g. multi-homography fitting) which easily frustrate other techniques. Experiments show that our method provides superior efficiency on various geometric model estimation tasks. Implementation of our algorithm is available on the authors, homepage.