
Skeletonisation Algorithms for Unorganised Point Clouds with Theoretical Guarantees
Real datasets often come in the form of an unorganised cloud of points. ...
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A fast approximate skeleton with guarantees for any cloud of points in a Euclidean space
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A fast and robust algorithm to count topologically persistent holes in noisy clouds
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Practical Shape Analysis and Segmentation Methods for Point Cloud Models
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Inferring Point Cloud Quality via Graph Similarity
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Point2Node: Correlation Learning of DynamicNode for Point Cloud Feature Modeling
Fully exploring correlation among points in point clouds is essential fo...
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Stratified Space Learning: Reconstructing Embedded Graphs
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Skeletonisation Algorithms with Theoretical Guarantees for Unorganised Point Clouds with High Levels of Noise
Data Science aims to extract meaningful knowledge from unorganised data. Real datasets usually come in the form of a cloud of points with only pairwise distances. Numerous applications require to visualise an overall shape of a noisy cloud of points sampled from a nonlinear object that is more complicated than a union of disjoint clusters. The skeletonisation problem in its hardest form is to find a 1dimensional skeleton that correctly represents a shape of the cloud. This paper compares several algorithms that solve the above skeletonisation problem for any point cloud and guarantee a successful reconstruction. For example, given a highly noisy point sample of an unknown underlying graph, a reconstructed skeleton should be geometrically close and homotopy equivalent to (has the same number of independent cycles as) the underlying graph. One of these algorithm produces a Homologically Persistent Skeleton (HoPeS) for any cloud without extra parameters. This universal skeleton contains subgraphs that provably represent the 1dimensional shape of the cloud at any scale. Other subgraphs of HoPeS reconstruct an unknown graph from its noisy point sample with a correct homotopy type and within a small offset of the sample. The extensive experiments on synthetic and real data reveal for the first time the maximum level of noise that allows successful graph reconstructions.
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