Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153439
Title: Approximate intrinsic voxel structure for point cloud simplification
Authors: Lv, Chenlei
Lin, Weisi
Zhao, Baoquan
Keywords: Engineering::Computer science and engineering::Computing methodologies::Computer graphics
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2021
Source: Lv, C., Lin, W. & Zhao, B. (2021). Approximate intrinsic voxel structure for point cloud simplification. IEEE Transactions On Image Processing, 30, 7241-7255. https://dx.doi.org/10.1109/TIP.2021.3104174
Project: MOE2016-T2-2-057(S)
Journal: IEEE Transactions on Image Processing
Abstract: A point cloud as an information-intensive 3D representation usually requires a large amount of transmission, storage and computing resources, which seriously hinder its usage in many emerging fields. In this paper, we propose a novel point cloud simplification method, Approximate Intrinsic Voxel Structure (AIVS), to meet the diverse demands in real-world application scenarios. The method includes point cloud preprocessing (denoising and down-sampling), AIVS-based realization for isotropic simplification and flexible simplification with intrinsic control of point distance. To demonstrate the effectiveness of the proposed AIVS-based method, we conducted extensive experiments by comparing it with several relevant point cloud simplification methods on three public datasets, including Stanford, SHREC, and RGB-D scene models. The experimental results indicate that AIVS has great advantages over peers in terms of moving least squares (MLS) surface approximation quality, curvature-sensitive sampling, sharp-feature keeping and processing speed.
URI: https://hdl.handle.net/10356/153439
ISSN: 1057-7149
DOI: 10.1109/TIP.2021.3104174
Rights: © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TIP.2021.3104174.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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