当前位置: X-MOL 学术ISPRS J. Photogramm. Remote Sens. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
SUM: A benchmark dataset of Semantic Urban Meshes
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 10.6 ) Pub Date : 2021-08-03 , DOI: 10.1016/j.isprsjprs.2021.07.008
Weixiao Gao 1 , Liangliang Nan 1 , Bas Boom 2 , Hugo Ledoux 1
Affiliation  

Recent developments in data acquisition technology allow us to collect 3D texture meshes quickly. Those can help us understand and analyse the urban environment, and as a consequence are useful for several applications like spatial analysis and urban planning. Semantic segmentation of texture meshes through deep learning methods can enhance this understanding, but it requires a lot of labelled data. The contributions of this work are three-fold: (1) a new benchmark dataset of semantic urban meshes, (2) a novel semi-automatic annotation framework, and (3) an annotation tool for 3D meshes. In particular, our dataset covers about 4 km2 in Helsinki (Finland), with six classes, and we estimate that we save about 600 h of labelling work using our annotation framework, which includes initial segmentation and interactive refinement. We also compare the performance of several state-of-the-art 3D semantic segmentation methods on the new benchmark dataset. Other researchers can use our results to train their networks: the dataset is publicly available, and the annotation tool is released as open-source.



中文翻译:

SUM:语义城市网格的基准数据集

数据采集​​技术的最新发展使我们能够快速采集 3D 纹理网格。这些可以帮助我们理解和分析城市环境,因此对空间分析和城市规划等多种应用很有用。通过深度学习方法对纹理网格进行语义分割可以增强这种理解,但它需要大量标记数据。这项工作的贡献有三方面:(1) 语义城市网格的新基准数据集,(2) 一种新颖的半自动注释框架,以及 (3) 3D 网格的注释工具。特别是,我们的数据集涵盖了大约 42在赫尔辛基(芬兰),有六个类,我们估计使用我们的注释框架可以节省大约 600 小时的标记工作,其中包括初始分割和交互式细化。我们还在新的基准数据集上比较了几种最先进的 3D 语义分割方法的性能。其他研究人员可以使用我们的结果来训练他们的网络:数据集是公开可用的,并且注释工具作为开源发布。

更新日期:2021-08-04
down
wechat
bug