ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916208601726976 |
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| author | Melekhov, Iaroslav Umashankar, Anand Kim, Hyeong-Jin Serkov, Vladislav Argyle, Dusty |
| author_facet | Melekhov, Iaroslav Umashankar, Anand Kim, Hyeong-Jin Serkov, Vladislav Argyle, Dusty |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2404_10699 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation Melekhov, Iaroslav Umashankar, Anand Kim, Hyeong-Jin Serkov, Vladislav Argyle, Dusty Computer Vision and Pattern Recognition 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. |
| title | ECLAIR: A High-Fidelity Aerial LiDAR Dataset for Semantic Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.10699 |