IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of methods for recovering quadrics in triangle meshes
ACM Computing Surveys (CSUR)
MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation
International Journal of Computer Vision
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Robust Adaptive-Scale Parametric Model Estimation for Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust moving least-squares fitting with sharp features
ACM SIGGRAPH 2005 Papers
Unsupervised, Information-Theoretic, Adaptive Image Filtering for Image Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
A consensus sampling technique for fast and robust model fitting
Pattern Recognition
Robust mesh reconstruction from unoriented noisy points
2009 SIAM/ACM Joint Conference on Geometric and Physical Modeling
An adaptive-scale robust estimator for motion estimation
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Range segmentation of large building exteriors: A hierarchical robust approach
Computer Vision and Image Understanding
Technical Section: Robust normal estimation for point clouds with sharp features
Computers and Graphics
CC-RANSAC: Fitting planes in the presence of multiple surfaces in range data
Pattern Recognition Letters
Adaptive-Scale robust estimator using distribution model fitting
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Computer Vision and Image Understanding
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Despite many successful applications of robust statistics, they have yet to be completely adapted to many computer vision problems. Range reconstruction, particularly in unstructured environments, requires a robust estimator that not only tolerates a large outlier percentage but also tolerates several discontinuities, extracting multiple surfaces in an image region. Observing that random outliers and/or points from across discontinuities increase a hypothesized fit's scale estimate (standard deviation of the noise), our new operator, called MUSE (Minimum Unbiased Scale Estimator), evaluates a hypothesized fit over potential inlier sets via an objective function of unbiased scale estimates. MUSE extracts the single best fit from the data by minimizing its objective function over a set of hypothesized fits and can sequentially extract multiple surfaces from an image region. We show MUSE to be effective on synthetic data modelling small scale discontinuities and in preliminary experiments on complicated range data.