EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDS

Laser scanning has become a well established surveying solution for obtaining 3D geo-spatial information on objects and environment. Nowadays scanners acquire up to millions of points per second which makes point cloud huge. Laser scanning is widely applied from airborne, carborne and stable platfor...

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Main Authors: J. Wang, R. Lindenbergh, M. Menenti
Format: Article
Language:English
Published: Copernicus Publications 2015-08-01
Series:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W5/25/2015/isprsannals-II-3-W5-25-2015.pdf
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author J. Wang
R. Lindenbergh
M. Menenti
author_facet J. Wang
R. Lindenbergh
M. Menenti
author_sort J. Wang
collection DOAJ
description Laser scanning has become a well established surveying solution for obtaining 3D geo-spatial information on objects and environment. Nowadays scanners acquire up to millions of points per second which makes point cloud huge. Laser scanning is widely applied from airborne, carborne and stable platforms, resulting in point clouds obtained at different attitudes and with different extents. Working with such different large point clouds makes the determination of their overlapping area necessary but often time consuming. In this paper, a scalable point cloud intersection determination method is presented based on voxels. The method takes two overlapping point clouds as input. It consecutively resamples the input point clouds according to a preset voxel cell size. For all non-empty cells the center of gravity of the points in contains is computed. Consecutively for those centers it is checked if they are in a voxel cell of the other point cloud. The same process is repeated after interchanging the role of the two point clouds. The quality of the results is evaluated by the distance to the pints from the other data set. Also computation time and quality of the results are compared for different voxel cell sizes. The results are demonstrated on determining he intersection between an airborne and carborne laser point clouds and show that the proposed method takes 0.10%, 0.15%, 1.26% and 14.35% of computation time compared the the classic method when using cell sizes of of 10, 8, 5 and 3 meters respectively.
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spelling doaj.art-f99f2e2c8be541b29861767d3557f53c2022-12-22T02:02:52ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502015-08-01II-3-W5253110.5194/isprsannals-II-3-W5-25-2015EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDSJ. Wang0R. Lindenbergh1M. Menenti2Dept. of Geoscience and Remote Sensing, Delft University of Technology Building 23, Stevinweg 1, Post Box 5048, 2628CN Delft, the NetherlandsDept. of Geoscience and Remote Sensing, Delft University of Technology Building 23, Stevinweg 1, Post Box 5048, 2628CN Delft, the NetherlandsDept. of Geoscience and Remote Sensing, Delft University of Technology Building 23, Stevinweg 1, Post Box 5048, 2628CN Delft, the NetherlandsLaser scanning has become a well established surveying solution for obtaining 3D geo-spatial information on objects and environment. Nowadays scanners acquire up to millions of points per second which makes point cloud huge. Laser scanning is widely applied from airborne, carborne and stable platforms, resulting in point clouds obtained at different attitudes and with different extents. Working with such different large point clouds makes the determination of their overlapping area necessary but often time consuming. In this paper, a scalable point cloud intersection determination method is presented based on voxels. The method takes two overlapping point clouds as input. It consecutively resamples the input point clouds according to a preset voxel cell size. For all non-empty cells the center of gravity of the points in contains is computed. Consecutively for those centers it is checked if they are in a voxel cell of the other point cloud. The same process is repeated after interchanging the role of the two point clouds. The quality of the results is evaluated by the distance to the pints from the other data set. Also computation time and quality of the results are compared for different voxel cell sizes. The results are demonstrated on determining he intersection between an airborne and carborne laser point clouds and show that the proposed method takes 0.10%, 0.15%, 1.26% and 14.35% of computation time compared the the classic method when using cell sizes of of 10, 8, 5 and 3 meters respectively.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W5/25/2015/isprsannals-II-3-W5-25-2015.pdf
spellingShingle J. Wang
R. Lindenbergh
M. Menenti
EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDS
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
title EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDS
title_full EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDS
title_fullStr EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDS
title_full_unstemmed EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDS
title_short EVALUATING VOXEL ENABLED SCALABLE INTERSECTION OF LARGE POINT CLOUDS
title_sort evaluating voxel enabled scalable intersection of large point clouds
url http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W5/25/2015/isprsannals-II-3-W5-25-2015.pdf
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