Abstract
We introduce ECLAIR (Extended Classification of Lidar for AI Recognition), a new outdoor large-scale aerial LiDAR dataset designed specifically for advancing research in point cloud semantic segmentation. As the most extensive and diverse collection of its kind to date, the dataset covers a total area of 10$km^2$ with close to 600 million points and features eleven distinct object categories. To guarantee the dataset's quality and utility, we have thoroughly curated the point labels through an internal team of experts, ensuring accuracy and consistency in semantic labeling. The dataset is engineered to move forward the fields of 3D urban modeling, scene understanding, and utility infrastructure management by presenting new challenges and potential applications. As a benchmark, we report qualitative and quantitative analysis of a voxel-based point cloud segmentation approach based on the Minkowski Engine.
Abstract (translated)
我们介绍了一个名为 ECLAIR（扩展分类激光雷达数据集）的新一代户外大型无人机激光雷达数据集，专门用于促进点云语义分割研究的进展。作为有史以来最广泛和多样化的数据集之一，该数据集涵盖了总面积为 10$km^2$，拥有近 600 万个点，并提供了十一条不同的物体类别。为了确保数据集的质量和实用性，我们通过内部专家团队对点标签进行了彻底审核，确保语义标注的准确性和一致性。该数据集通过呈现新的挑战和潜在应用，推动了三维城市建模、场景理解和实用基础设施管理领域的发展。作为基准，我们报道了基于Minkowski引擎的体素点云分割方法的定性和定量分析。
URL
https://arxiv.org/abs/2404.10699
