Approximate intrinsic voxel structure for point cloud simplification

• Main Authors: Lv, Chenlei, Lin, Weisi, Zhao, Baoquan
• 其他作者: School of Computer Science and Engineering
• 格式: Article
• 語言: English
• 出版: 2021
• 主題: Engineering::Computer science and engineering::Computing methodologies::Computer graphics; Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision; Point Cloud Simplification; Isotropic Simplification
• 在線閱讀: https://hdl.handle.net/10356/153439

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.