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
TL;DR: This work develops a simple data‐driven method for removing outliers and reducing noise in unordered point clouds using a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds.
A texture descriptor for browsing and similarity retrieval
TL;DR: A texture descriptor based on a multiresolution decomposition using Gabor wavelets is proposed that is quite robust to illumination variations and compares favorably with other texture descriptors for similarity retrieval.
3D Point Cloud Denoising Using Graph Laplacian Regularization of a Low Dimensional Manifold Model
TL;DR: This paper extends a previously proposed low-dimensional manifold model for the image patches to surface patches in the point cloud, and seeks self-similar patches to denoise them simultaneously using the patch manifold prior, and proposes a new discrete patch distance measure to quantify the similarity between two same-sized surface patches for graph construction that is robust to noise.
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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.
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