Journal Article10.1016/J.IMAGE.2017.05.009
A review of algorithms for filtering the 3D point cloud
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TL;DR: This paper makes an attempt to present a comprehensive analysis of the state-of-the-art methods for filtering point cloud, categorized into seven classes, which concentrate on their common and obvious traits.
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Abstract: In recent years, 3D point cloud has gained increasing attention as a new representation for objects However, the raw point cloud is often noisy and contains outliers Therefore, it is crucial to remove the noise and outliers from the point cloud while preserving the features, in particular, its fine details This paper makes an attempt to present a comprehensive analysis of the state-of-the-art methods for filtering point cloud The existing methods are categorized into seven classes, which concentrate on their common and obvious traits An experimental evaluation is also performed to demonstrate robustness, effectiveness and computational efficiency of several methods used widely in practice
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Citations
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds
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A texture descriptor for browsing and similarity retrieval
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3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model
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A Survey of Mobile Laser Scanning Applications and Key Techniques over Urban Areas
TL;DR: A comprehensive survey of urban applications and key techniques based on MLS point clouds is conducted, including classification methods, object recognition, data registration, data fusion, and 3D city modeling.
128
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Feature-Preserving Surface Reconstruction From Unoriented, Noisy Point Data
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Adaptive simplification of point cloud using k-means clustering
Bao-Quan Shi,Jin Liang,Qing Liu +2 more
- 01 Aug 2011
TL;DR: A new adaptive simplification method to reduce the number of the scanned dense points by employing the k -means clustering algorithm to gather similar points together in the spatial domain and uses the maximum normal vector deviation as a measure of cluster scatter to partition the gathered point sets into a series of sub-clusters in the feature field.
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