Consolidation of unorganized point clouds for surface reconstruction

By Hui Huang, Dan Li, Hao Zhang, Uri M. Ascher, Daniel Cohen-Or. 2009

522 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design

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In Computational Mechanics

In Computer Graphics and Computer-Aided Design

Citations in the corpus

PCPNet Learning Local Shape Properties from Raw Point Clouds (2017, background): “As a result, they have has been extended signiﬁcantly in recent years both through more robust estimates [HLZ ∗ 09] and global techniques based on signed distance computations [MDGD ∗ 10], among others.”
Point Cloud Skeletons via Laplacian Based Contraction (2010, unknown): “Close-by structures present difficulties for most pointcloud processing algorithms [51].”
PU-GAN: A Point Cloud Upsampling Adversarial Network (2019, methodology): “Early methods [3, 21, 14, 15, 33] for the problem are optimization-based, where various shape priors are used to constrain the point cloud generation.”
Deep Learning for Robust Normal Estimation in Unstructured Point Clouds (2016, methodology): “Improvements have been proposed to address robustness to out-liers and non-uniformity, using adaptive weights [HLZ ∗ 09].”
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review (2020, background): “Besides, the orientation of point sets is missing, which poses a great challenge for object pattern recognition [53].”
Deep points consolidation (2015, methodology): “Our approach is derived from previous ones [Lipman et al. 2007; Huang et al. 2009], but with important differences.”
A review of algorithms for filtering the 3D point cloud (2017, unknown): “Huang et al. [49] incorporated locally adaptive density weights of each point into LOP, that was WLOP (see Fig.”
Patch-Based Progressive 3D Point Set Upsampling (2018, methodology): “[23] employ an anisotropic locally optimal projection (LOP) operator [22,36] to consolidate and push points away from the edges, followed by a progressive edge-aware upsampling procedure.”
Edge-aware point set resampling (2013, methodology): “To this end, we alter the LOP operator [Lipman et al. 2007b; Huang et al. 2009] and make it normal or edge aware, allowing for resampling away from edges.”
EC-Net: an Edge-aware Point set Consolidation Network (2018, background): “Early works in this area assumed smooth surface [1,14,15].”
PU-Net: Point Cloud Upsampling Network (2018, methodology): “Successively, Huang et al. [11] proposed an improved weighted LOP to address the point set density problem.”
P2P-NET: Bidirectional Point Displacement Net for Shape Transform (methodology): “Representative methods include point cloud consolidation via LOP [Lipman et al. 2007] and WLOP [Huang et al. 2009; Preiner et al. 2014].”