TY - GEN
T1 - CPU-based real-time surface and solid voxelization for incomplete point cloud
AU - Garcia, Frederic
AU - Ottersten, Bjorn
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/12/4
Y1 - 2014/12/4
N2 - 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.
AB - 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.
KW - Curve-skeleton
KW - Distance transform
KW - Point cloud
KW - Real-time
KW - Skeletonization
KW - Voxelization
UR - http://www.scopus.com/inward/record.url?scp=84919940808&partnerID=8YFLogxK
U2 - 10.1109/ICPR.2014.475
DO - 10.1109/ICPR.2014.475
M3 - Conference contribution
AN - SCOPUS:84919940808
T3 - Proceedings - International Conference on Pattern Recognition
SP - 2757
EP - 2762
BT - Proceedings - International Conference on Pattern Recognition
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd International Conference on Pattern Recognition, ICPR 2014
Y2 - 24 August 2014 through 28 August 2014
ER -