2013
DOI: 10.1016/j.imavis.2013.07.007
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Image-consistent patches from unstructured points with J-linkage

Abstract: Going from unstructured cloud of points to surfaces is a challenging problem. However, as points are produced by a structure-and-motion pipeline, image-consistency is a powerful clue that comes to the rescue. In this paper we present a method for extracting planar patches from an unstructured cloud of points, based on the detection of image-consistent planar patches with J-linkage, a robust algorithm for multiple models fitting. The method integrates several constraints inside J-linkage, optimizes the position… Show more

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Cited by 12 publications
(11 citation statements)
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References 41 publications
(48 reference statements)
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“…x∈U PS (x). (5) and the distance between clusters is computed as the Jaccard distance [22] between the respective preference representations. The Jaccard distance between two sets A, B is defined as…”
Section: Preference Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…x∈U PS (x). (5) and the distance between clusters is computed as the Jaccard distance [22] between the respective preference representations. The Jaccard distance between two sets A, B is defined as…”
Section: Preference Analysismentioning
confidence: 99%
“…A typical example of this problem can be found in 3D reconstruction, where multi-model fitting is employed either to estimate multiple rigid moving objects and hence to initialize multi-body Structure from Motion [1,2], or to produce intermediate geometric interpretations of reconstructed 3D point cloud by fitting geometric primitives [3,4,5]. Other scenarios in which the estimation of multiple geometric structure plays a primary role include face clustering, body-pose estimation, augmented reality, image stitching and video motion segmentation, to name just a few.…”
mentioning
confidence: 99%
“…Building on this idea, the J-Linkage algorithm [8,13] was the first successful application of a preference-based representation of data. A two steps first-represent-thencluster scheme is implemented: data are represented by the votes they grant to a set of model hypotheses, then a greedy agglomerative clustering is performed to obtain a partition of the data.…”
Section: Preference Analysismentioning
confidence: 99%
“…Hence, the preference analysis-based method first generates a large number of hypotheses by sampling a minimum sample set (MSS), and then performs preference analysis on the hypotheses residuals. The most classical method, J-linkage [20,21,33], adopts the binarized conceptual preference of points, which binarizes the residuals by inlier threshold, and introduces the Jaccard distance to conduct linkage clustering of the point preferences, thus the inliers are segmented into different clusters. Similar to J-linkage, T-linkage [22,24,25] uses relaxation of the binary preference function and the soft Tanimoto distance to improve the conceptual preference in J-linkage for better clustering.…”
Section: Introductionmentioning
confidence: 99%