{"url":"/dataset/kitti-odometry-benchmark","name":"KITTI Odometry Benchmark","full_name":null,"description_markdown":"The odometry benchmark consists of 22 stereo sequences, saved in loss less png format: We provide 11 sequences (00-10) with ground truth trajectories for training and 11 sequences (11-21) without ground truth for evaluation. For this benchmark you may provide results using monocular or stereo visual odometry, laser-based SLAM or algorithms that combine visual and LIDAR information. The only restriction we impose is that your method is fully automatic (e.g., no manual loop-closure tagging is allowed) and that the same parameter set is used for all sequences. A development kit provides details about the data format. \r\nMore details are available at: https://www.cvlibs.net/datasets/kitti/eval_odometry.php.","description_withheld":null,"homepage":"https://www.cvlibs.net/datasets/kitti/eval_odometry.php","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"Camera Pose Estimation","url":"/task/camera-pose-estimation","datasets_with_task":"/datasets/task/camera-pose-estimation"}],"languages":[],"variants":["KITTI Odometry Benchmark"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/camera-pose-estimation-on-kitti-odometry","task":"Camera Pose Estimation","dataset_variant":"KITTI Odometry Benchmark","rows":7,"metrics":["Average Translational Error et[%]","Average Rotational Error er[%]","Absolute Trajectory Error [m]"],"first_row_in_archive_order":{"model":"Manydepth2","paper":"/paper/mgdepth-motion-guided-cost-volume-for-self","metrics":{"Average Rotational Error er[%]":"2.205","Average Translational Error et[%]":"7.15"},"code_links":[{"title":"kaichen-z/rad","url":"https://github.com/kaichen-z/rad"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/scipad-incorporating-spatial-clues-into","title":"SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning","date":"2024-07-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mgdepth-motion-guided-cost-volume-for-self","title":"Manydepth2: Motion-Aware Self-Supervised Multi-Frame Monocular Depth Estimation in Dynamic Scenes","date":"2023-12-23","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-scale-consistent-depth-and-ego","title":"Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video","date":"2019-08-28","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/beyond-photometric-loss-for-self-supervised","title":"Beyond Photometric Loss for Self-Supervised Ego-Motion Estimation","date":"2019-02-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/digging-into-self-supervised-monocular-depth","title":"Digging Into Self-Supervised Monocular Depth Estimation","date":"2018-06-04","rows_on_this_dataset":1,"code_links":15,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":17,"samples_unverified":7,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/geonet-unsupervised-learning-of-dense-depth","title":"GeoNet: Unsupervised Learning of Dense Depth, Optical Flow and Camera Pose","date":"2018-03-06","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-learning-of-depth-and-ego-motion-1","title":"Unsupervised Learning of Depth and Ego-Motion from Video","date":"2017-04-25","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":44,"samples_ran":19,"samples_unverified":25,"pointer_only_for_licence":9,"papers_with_no_sample_that_ran":2,"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."}