ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation
Iaroslav Melekhov, Anand Umashankar, Hyeong-Jin Kim, Vladislav Serkov, Dusty Argyle
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 10km<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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. We release the dataset as open-source and it can be accessed at https://github.com/SharperShape/eclair-dataset