{"url":"/dataset/lidar-mos","name":"LiDAR-MOS","full_name":"LiDAR-based Moving Object Segmentation","description_markdown":"# Tasks.\r\nIn moving object segmentation of point cloud sequences, one has to provide motion labels for each point of the test sequences 11-21. Therefore, the input to all evaluated methods is a list of coordinates of the three-dimensional points along with their remission, i.e., the strength of the reflected laser beam which depends on the properties of the surface that was hit. Each method should then output a label for each point of a scan, i.e., one full turn of the rotating LiDAR sensor. Here, we only distinguish between static and moving object classes.\r\n\r\n# Metric\r\nTo assess the labeling performance, we rely on the commonly applied Jaccard Index or intersection-over-union (mIoU) metric over moving parts of the environment. We map all moving-x classes of the original SemanticKITTI semantic segmentation benchmark to a single moving object class.\r\n\r\n# Citation\r\nCitation. More information on the task and the metric, you can find in our publication related to the task:\r\n@article{chen2021ral,\r\n  title={{Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data}},\r\n  author={X. Chen and S. Li and B. Mersch and L. Wiesmann and J. Gall and J. Behley and C. Stachniss},\r\n  year={2021},\r\n  journal={IEEE Robotics and Automation Letters(RA-L)},\r\n  doi = {10.1109/LRA.2021.3093567}\r\n}","description_withheld":null,"homepage":"https://competitions.codalab.org/competitions/28894","introduced_date":"2021-05-19","introduced_date_note":null,"introduced_by":{"paper":"/paper/moving-object-segmentation-in-3d-lidar-data-a","title":"Moving Object Segmentation in 3D LiDAR Data: A Learning-based Approach Exploiting Sequential Data","first_author":null,"url":null},"license":null,"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"},{"name":"3D Part Segmentation","url":"/task/3d-part-segmentation","datasets_with_task":"/datasets/task/3d-part-segmentation"},{"name":"Moving Point Cloud Processing","url":"/task/moving-point-cloud-processing","datasets_with_task":"/datasets/task/moving-point-cloud-processing"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LiDAR-MOS"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}