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

Iaroslav Melekhov, Anand Umashankar, Hyeong-Jin Kim, Vladislav Serkov, Dusty Argyle
4/16/2024
cs.CV

No official code found. Best implementation: SharperShape/eclair-dataset (49 stars, MIT, last commit 2025-05-07).

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.

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Code Implementations(1)

SharperShape/eclair-datasetCommunity100%
MIT

Home of the open source ECLAIR dataset

499Python, DockerfileApr 12, 20241 years agoMIT

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Cite this paper

@article{melekhov2024eclair,
  title  = {ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation},
  author = {Iaroslav Melekhov and Anand Umashankar and Hyeong-Jin Kim and Vladislav Serkov and Dusty Argyle},
  year   = {2024},
  eprint = {2404.10699},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.10699v1}
}

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