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.

翻译：我们提出ECLAIR（面向人工智能识别的高精度机载LiDAR扩展分类）——一种专为推进点云语义分割研究而设计的新型室外大规模机载LiDAR数据集。作为迄今同类数据集中规模最大、多样性最丰富的数据集，其覆盖总面积达10$km^2$，包含近6亿个点，并涵盖11个不同物体类别。为确保数据集的质量与实用性，我们通过内部专家团队对点标签进行了严格筛选，保证了语义标注的准确性与一致性。该数据集通过呈现全新挑战与潜在应用场景，旨在推动三维城市建模、场景理解及公共基础设施管理领域的发展。作为基准实验，我们报告了基于Minkowski Engine的体素点云分割方法的定性与定量分析结果。