IEEE Transactions on Pattern Analysis and Machine Intelligence
Consistency of robust estimators in multi-structural visual data segmentation
Pattern Recognition
Automatic segmentation of unorganized noisy point clouds based on the Gaussian map
Computer-Aided Design
A New Segmentation Approach for Old Fractured Pieces
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Planar background elimination in range images: a practical approach
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Efficient distance estimation for fitting implicit quadric surfaces
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Automatic face segmentation and facial landmark detection in range images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Segmenting free-form 3d objects by a function representation in spherical coordinates
ISCIS'06 Proceedings of the 21st international conference on Computer and Information Sciences
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This paper presents a novel range image segmentation method employing an improved robust estimator to iteratively detect and extract distinct planar and quadric surfaces. Our robust estimator extends M-estimator Sample Consensus/Random Sample Consensus (MSAC/RANSAC) to use local surface orientation information, enhancing the accuracy of inlier/outlier classification when processing noisy range data describing multiple structures. An efficient approximation to the true geometric distance between a point and a quadric surface also contributes to effectively reject weak surface hypotheses and avoid the extraction of false surface components. Additionally, a genetic algorithm was specifically designed to accelerate the optimization process of surface extraction, while avoiding premature convergence. We present thorough experimental results with quantitative evaluation against ground truth. The segmentation algorithm was applied to three real range image databases and competes favorably against eleven other segmenters using the most popular evaluation framework in the literature. Our approach lends itself naturally to parallel implementation and application in real-time tasks. The method fits well into several of today's applications in man-made environments, such as target detection and autonomous navigation, for which obstacle detection, but not description or reconstruction, is required. It can also be extended to process point clouds resulting from range image registration.