VxH:系统地确定有效的分层体素结构

IF 1.2 Q4 REMOTE SENSING
Mouad Rifai, Lennart Johnsson
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引用次数: 1

摘要

拥有数百万到数十亿个点的3D地图现在被越来越多的应用程序所使用,其处理速度在每秒数十万到数百万个点。在移动应用程序中,管理此类数据并从中提取有用信息的功率和能源消耗是关键问题。我们已经开发了结构和方法,目的是最大限度地减少内存使用和相关的能量消耗,用于体素化点云的索引和序列化。在我们的情况下,点的主要来源是机载激光扫描,但我们的方法并不仅限于这样的设置。我们的仿真结果表明,与主要的机载激光雷达数据压缩方案LASzip相比,该压缩方案的内存使用减少系数大约是Octree/Octomap压缩方案的200倍,文件大小减少系数高达1.65倍。此外,我们的结构可以显著提高处理效率,因为它们包含在捕获几何方面的层次结构中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
VxH: A systematic determination of efficient hierarchical voxel structures
3D maps with many millions to billions of points are now used in an increasing number of applications, with processing rates in the hundreds of thousands to millions of points per second. In mobile applications, power and energy consumption for managing such data and extracting useful information thereof are critical concerns. We have developed structures and methodologies with the purpose of minimizing memory usage and associated energy consumption for indexing and serialization of voxelized point-clouds. The primary source of points in our case is airborne laser scanning, but our methodology is not restricted to only such setting. Our emulated results show a memory usage reduction factor of roughly up to 200 × that of Octree/Octomap, and a file size reduction factor of up to 1.65 × compared the predominating compression scheme for airborne Lidar data, LASzip. In addition, our structures enable significantly more efficient processing since they are included in a hierarchical structure that captures geometric aspects.
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来源期刊
CiteScore
4.40
自引率
5.30%
发文量
43
期刊介绍: ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.
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