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Papers/ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic ...

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

Iaroslav Melekhov, Anand Umashankar, Hyeong-Jin Kim, Vladislav Serkov, Dusty Argyle

2024-04-16Scene UnderstandingSegmentationSemantic SegmentationManagementPoint Cloud Segmentation3D Semantic Segmentation
PaperPDFCode(official)

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.

Results

TaskDatasetMetricValueModel
Semantic SegmentationECLAIRF10.845Res16UNet14C
Semantic SegmentationECLAIRMean IoU0.7729Res16UNet14C
3D Semantic SegmentationECLAIRF10.845Res16UNet14C
3D Semantic SegmentationECLAIRMean IoU0.7729Res16UNet14C
10-shot image generationECLAIRF10.845Res16UNet14C
10-shot image generationECLAIRMean IoU0.7729Res16UNet14C

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