Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/149096
Title: | Voxel structure-based mesh reconstruction from a 3D point cloud | 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). Voxel structure-based mesh reconstruction from a 3D point cloud. IEEE Transactions On Multimedia. https://dx.doi.org/10.1109/TMM.2021.3073265 | Project: | MOE2016-T2-2-057(S) | Journal: | IEEE Transactions on Multimedia | Abstract: | Mesh reconstruction from a 3D point cloud is an important topic in the fields of computer graphic, computer vision, and multimedia analysis. In this paper, we propose a voxel structure-based mesh reconstruction framework. It provides the intrinsic metric to improve the accuracy of local region detection. Based on the detected local regions, an initial reconstructed mesh can be obtained. With the mesh optimization in our framework, the initial reconstructed mesh is optimized into an isotropic one with the important geometric features such as external and internal edges. The experimental results indicate that our framework shows great advantages over peer ones in terms of mesh quality, geometric feature keeping, and processing speed. | URI: | https://hdl.handle.net/10356/149096 | ISSN: | 1520-9210 | DOI: | 10.1109/TMM.2021.3073265 | Schools: | School of Computer Science and Engineering | 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/TMM.2021.3073265. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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FINAL VERSION.pdf | Accepted version of the paper | 28.38 MB | Adobe PDF | ![]() View/Open |
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