{"url":"/dataset/rellis-3d","name":"RELLIS-3D","full_name":null,"description_markdown":"**RELLIS-3D** is a multi-modal dataset for off-road robotics. It was collected in an off-road environment containing annotations for 13,556 LiDAR scans and 6,235 images. The data was collected on the Rellis Campus of Texas A&M University and presents challenges to existing algorithms related to class imbalance and environmental topography. The dataset also provides full-stack sensor data in ROS bag format, including RGB camera images, LiDAR point clouds, a pair of stereo images, high-precision GPS measurement, and IMU data.\n\nSource: [https://github.com/unmannedlab/RELLIS-3D](https://github.com/unmannedlab/RELLIS-3D)\nImage Source: [https://github.com/unmannedlab/RELLIS-3D](https://github.com/unmannedlab/RELLIS-3D)","description_withheld":null,"homepage":"https://github.com/unmannedlab/RELLIS-3D","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/rellis-3d-dataset-data-benchmarks-and","title":"RELLIS-3D Dataset: Data, Benchmarks and Analysis","first_author":"Peng Jiang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"},{"name":"Scene Understanding","url":"/task/scene-understanding","datasets_with_task":"/datasets/task/scene-understanding"},{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"},{"name":"Autonomous Navigation","url":"/task/autonomous-navigation","datasets_with_task":"/datasets/task/autonomous-navigation"}],"languages":[],"variants":["RELLIS-3D Dataset","RELLIS-3D"],"data_loaders":[{"repo":"https://github.com/unmannedlab/RELLIS-3D","url":"https://github.com/unmannedlab/RELLIS-3D","frameworks":["pytorch"]}],"num_papers_in_archive":57,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-rellis-3d-dataset","task":"Semantic Segmentation","dataset_variant":"RELLIS-3D Dataset","rows":5,"metrics":["Mean IoU (class)"],"first_row_in_archive_order":{"model":"Swiftnet","paper":"/paper/semantic-segmentation-with-high-inference","metrics":{"Mean IoU (class)":"77.9"},"code_links":[{"title":"cufctl/segmentation","url":"https://github.com/cufctl/segmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-semantic-segmentation-on-rellis-3d-dataset","task":"3D Semantic Segmentation","dataset_variant":"RELLIS-3D Dataset","rows":4,"metrics":["Mean IoU (class)"],"first_row_in_archive_order":{"model":"Cylinder3D","paper":"/paper/comparison-of-lidar-semantic-segmentation","metrics":{"Mean IoU (class)":"46.07"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/2d-semantic-segmentation-on-rellis-3d","task":"2D Semantic Segmentation","dataset_variant":"RELLIS-3D","rows":2,"metrics":["Mean IoU (class)"],"first_row_in_archive_order":{"model":"Swiftnet","paper":"/paper/semantic-segmentation-with-high-inference","metrics":{"Mean IoU (class)":"77.9"},"code_links":[{"title":"cufctl/segmentation","url":"https://github.com/cufctl/segmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/comparison-of-lidar-semantic-segmentation","title":"Comparison of lidar semantic segmentation performance on the structured SemanticKITTI and off-road RELLIS-3D datasets","date":"2024-08-20","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/utilizing-neural-networks-for-semantic","title":"Utilizing Neural Networks for Semantic Segmentation on RGB/LiDAR Fused Data for Off-road Autonomous Military Vehicle Perception","date":"2023-04-10","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/semantic-segmentation-with-high-inference","title":"Semantic Segmentation with High Inference Speed in Off-Road Environments","date":"2023-04-10","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/ganav-group-wise-attention-network-for","title":"GANav: Efficient Terrain Segmentation for Robot Navigation in Unstructured Outdoor Environments","date":"2021-03-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rellis-3d-dataset-data-benchmarks-and","title":"RELLIS-3D Dataset: Data, Benchmarks and Analysis","date":"2020-11-17","rows_on_this_dataset":4,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"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."}