Technical Section: Robust normal estimation for point clouds with sharp features
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An efficient RANSAC for 3D object recognition in noisy and occluded scenes
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An M-estimator for high breakdown robust estimation in computer vision
Computer Vision and Image Understanding
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Generalized kernel density estimation based robust estimator and its application
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Efficient and robust model fitting with unknown noise scale
Image and Vision Computing
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In this paper, we present a new Adaptive-Scale Kernel Consensus (ASKC) robust estimator as a generalization of the popular and state-of-the-art robust estimators such as RANdom SAmple Consensus (RANSAC), Adaptive Scale Sample Consensus (ASSC), and Maximum Kernel Density Estimator (MKDE). The ASKC framework is grounded on and unifies these robust estimators using nonparametric kernel density estimation theory. In particular, we show that each of these methods is a special case of ASKC using a specific kernel. Like these methods, ASKC can tolerate more than 50 percent outliers, but it can also automatically estimate the scale of inliers. We apply ASKC to two important areas in computer vision, robust motion estimation and pose estimation, and show comparative results on both synthetic and real data.