Cenagis-als基准——基于对三维点云分割的数据集和基准的回顾,提出了密集als基准的新方案

P. Zachar, K. Bakuła, W. Ostrowski
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引用次数: 0

摘要

摘要标杆管理是科技进步的重要工具。本文回顾了三维点云分割和分类的基准。根据对文章和收集到的知识的分析,可以得出结论,基准的数量有所增加,可以将研究结果与特定的性能指标进行独立比较。但是,基准在类的数量、空间大小、命名法和类划分方面有所不同。在本文中,我们介绍了一个新的带注释的三维数据集- CENAGIS-ALS Benchmark。我们提出了一个由Leica CityMapper-2为波兰华沙中心获取的高密度激光雷达点云的基准。面积为2 km2,数据密度为275 pts/m2。数据集由许多类组成,这些类对于这种类型的数据是可区分的。除了基本的类别之外,从城市空间的角度来看,更重要的专业类别也被区分开来。此外,类的划分由三个层次的细节组成,从粗糙的(例如,建筑物)到精细的元素(例如,屋顶,烟囱和其他屋顶物体)。考虑到与其他基准数据相比,具有统一数据质量的研究区域的大空间大小以及具有分层划分的类的数量较多,该基准可以为地理空间社会做出贡献。
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CENAGIS-ALS BENCHMARK - NEW PROPOSAL FOR DENSE ALS BENCHMARK BASED ON THE REVIEW OF DATASETS AND BENCHMARKS FOR 3D POINT CLOUD SEGMENTATION
Abstract. Benchmarking is an essential tool for scientific and technological progress. This article reviews the benchmarks for 3D point cloud segmentation and classification. Based on the analysis of the articles and the knowledge gathered, it can be concluded that there has been an increase in the number of benchmarks, allowing to compare research results against specific performance metrics independently. However, benchmarks vary regarding the number of classes, spatial size, nomenclature, and class division. In this article, we introduce a new annotated 3D dataset - CENAGIS-ALS Benchmark. We propose a benchmark of highly dense lidar point clouds acquired by Leica CityMapper-2 for the Centre of Warsaw, Poland. The area covers 2 km2, and the data has a density of 275 pts/m2. The dataset consists of a number of classes that are distinguishable for this type of data. In addition to the basic classes, more specialized classes, important from the perspective of urban space, are also distinguished. Moreover, the division of classes consists of three levels of detail from coarse (e.g., a building) to refined elements (e.g., roofs, chimneys, and other rooftop objects). This benchmark can contribute to geospatial societies, considering the large spatial size of the study area with unified data quality and the higher number of classes with the hierarchical division compared to other benchmarking data.
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来源期刊
CiteScore
1.70
自引率
0.00%
发文量
949
审稿时长
16 weeks
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