论文标题
多伦多-3D：用于城市道路语义分割的大型移动激光雷达数据集
Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways
论文作者
论文摘要
大规模室外点云的语义分割对于各种应用中的城市场景理解至关重要，尤其是自动驾驶和城市高清（HD）映射。随着移动激光扫描（MLS）系统的快速开发，可以使用大量的点云可供场景理解，但是公开可访问的大型标签数据集（对于开发基于学习的方法都是必不可少的）仍然有限。本文介绍了多伦多-3D，这是一个由MLS系统在加拿大多伦多收购的大型城市户外云数据集进行语义细分。该数据集涵盖了大约1公里的点云，并由8个标记的对象类组成约7830万点。进行了语义分割的基线实验，结果证实了该数据集有效训练深度学习模型的能力。多伦多-3D的发布是为了鼓励新的研究，并通过研究界的反馈来改进和更新标签。
Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released to encourage new research, and the labels will be improved and updated with feedback from the research community.
