Reference : CPU-based real-time surface and solid voxelization for incomplete point cloud
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/10993/25019
CPU-based real-time surface and solid voxelization for incomplete point cloud
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Garcia, Frederic D. [Interdisciplinary Centre for Security Reliability and Trust (SnT), University of Luxembourg, Luxembourg]
Ottersten, Björn mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) >]
2014
Proceedings of the 22nd International Conference on Pattern Recognition
Institute of Electrical and Electronics Engineers Inc.
2757-2762
Yes
International
22nd International Conference on Pattern Recognition, ICPR 2014
24 August 2014 through 28 August 2014
[en] Curve-skeleton ; Distance transform ; Point cloud ; Real-time ; Skeletonization ; Voxelization ; Computer graphics ; Computer vision ; Pattern recognition ; Curve skeletons ; Distance transforms ; Real time ; Three dimensional computer graphics
[en] This paper presents a surface and solid voxelization approach for incomplete point cloud datasets. Voxelization stands for a discrete approximation of 3-D objects into a volumetric representation, a process which is commonly employed in computer graphics and increasingly being used in computer vision. In contrast to surface voxelization, solid voxelization not only set those voxels related to the object surface but also those voxels considered to be inside the object. To that end, we first approximate the given point set, usually describing the external object surface, to an axis-aligned voxel grid. Then, we slice-wise construct a shell containing all surface voxels along each grid-axis pair. Finally, voxels inside the constructed shell are set. Solid voxelization results from the combination of all slices, resulting in a watertight and gap-free representation of the object. The experimental results show a high performance when voxelizing point cloud datasets, independently of the object's complexity, robust to noise, and handling large portions of data missing. © 2014 IEEE.
http://hdl.handle.net/10993/25019
10.1109/ICPR.2014.475
109641
9781479952083

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