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Approximate Intrinsic Voxel Structure for Point Cloud Simplification
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DOI：10.1109/TIP.2021.3104174.png)
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摘要
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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 pre-processing (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. The source code of the proposed method is publicly available. (https://github.com/vvvwo/AIVS-project).
Keyword:
Three-dimensional displays
Image coding
Task analysis
Surface treatment
Pipelines
Noise reduction
Feature extraction
Point cloud simplification
isotropic simplification
intrinsic control
MLS surface
curvature-sensitive sampling
sharp-feature keeping
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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