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Out-of-core real-time visualization of massive 3D point clouds

Published: 21 June 2010 Publication History

Abstract

This paper presents a point-based rendering approach to visualize massive sets of 3D points in real-time. In many disciplines such as architecture, engineering, and archeology LiDAR technology is used to capture sites and landscapes; the resulting massive 3D point clouds pose challenges for traditional storage, processing, and presentation techniques. The available hardware resources of CPU and GPU are limited, and the 3D point cloud data exceeds available memory size in general. Hence out-of-core strategies are required to overcome the limit of memory. We discuss concepts and implementations of rendering algorithms and interaction techniques that make out-of-core real-time visualization and exploration of massive 3D point clouds feasible. We demonstrate with our implementation real-time visualization of arbitrarily sized 3D point clouds with current PC hardware using a spatial data structure in combination with a point-based rendering algorithm. A rendering front is used to increase the performance taking into account user interaction as well as available hardware resources. Furthermore, we evaluate our approach, describe its characteristics, and report on applications.

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cover image ACM Conferences
AFRIGRAPH '10: Proceedings of the 7th International Conference on Computer Graphics, Virtual Reality, Visualisation and Interaction in Africa
June 2010
153 pages
ISBN:9781450301183
DOI:10.1145/1811158
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 June 2010

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Author Tags

  1. 3D point clouds
  2. LiDAR
  3. out-of-core visualization
  4. point-based rendering

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Overall Acceptance Rate 47 of 90 submissions, 52%

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  • (2023)Rendering massive indoor point clouds in virtual realityVirtual Reality10.1007/s10055-023-00766-327:3(1859-1874)Online publication date: 3-Mar-2023
  • (2022)Parallelization Strategies for Hierarchical Density-Based Clustering Algorithm Using OpenMP for Scan-To-BIM ApplicationsProceedings of the Canadian Society of Civil Engineering Annual Conference 202110.1007/978-981-19-0968-9_43(541-552)Online publication date: 26-May-2022
  • (2020)Fast Out‐of‐Core Octree Generation for Massive Point CloudsComputer Graphics Forum10.1111/cgf.1413439:7(155-167)Online publication date: 24-Nov-2020
  • (2019)Dictionary Compression in Point Cloud Data ManagementACM Transactions on Spatial Algorithms and Systems10.1145/32997705:1(1-25)Online publication date: 6-Jun-2019
  • (2019)A demonstration of B-EagleV Visualizing massive point cloud directly from HDFS2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9005717(4121-4124)Online publication date: Dec-2019
  • (2018)Service-Oriented Processing and Analysis of Massive Point Clouds in Geoinformation ManagementService-Oriented Mapping10.1007/978-3-319-72434-8_2(43-61)Online publication date: 8-Jun-2018
  • (2017)Nationwide Point Cloud—The Future Topographic Core DataISPRS International Journal of Geo-Information10.3390/ijgi60802436:8(243)Online publication date: 8-Aug-2017
  • (2017)Dictionary Compression in Point Cloud Data ManagementProceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/3139958.3139969(1-10)Online publication date: 7-Nov-2017
  • (2017)A Visualization-Based Analysis System for Urban Search & Rescue Mission Planning SupportComputer Graphics Forum10.1111/cgf.1286936:6(148-159)Online publication date: 1-Sep-2017
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