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.

ECLAIR 是一个新的室外大规模航空激光雷达数据集，它旨在推动点云语义分割的研究。该数据集是迄今为止最全面和多样化的，覆盖面积为 10 平方公里，包含近 6 亿个点，并包含 11 个不同的物体类别。作为一个基准测试，我们使用 Minkowski 引擎对基于体素的点云分割方法进行定性和定量分析。

ECLAIR：用于语义分割的高保真航空 LiDAR 数据集

ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation

In this preliminary work we attempt to apply submanifold sparse convolution
to the task of 3D person detection. In particular, we present Person-MinkUNet,
a single-stage 3D person detection network based on Minkowski Engine with U-Net
architecture. The network achieves a 76.4% average precision (AP) on the JRDB
3D detection benchmark.

本研究利用次流形稀疏卷积，探索应用于三维人体检测任务。我们提出了基于 Minkowski 引擎和 U-Net 架构的单级三维人体检测网络 Person-MinkUNet，并在 JRDB 三维检测基准测试中实现了 76.4％的平均精度（AP）