{"url":"/dataset/ocid","name":"OCID","full_name":"Object Clutter Indoor Dataset","description_markdown":"Developing robot perception systems for handling objects in the real-world requires computer vision algorithms to be carefully scrutinized with respect to the expected operating domain. This demands large quantities of ground truth data to rigorously evaluate the performance of algorithms.\r\n\r\nThe Object Cluttered Indoor Dataset is an RGBD-dataset containing point-wise labeled point-clouds for each object. The data was captured using two ASUS-PRO Xtion cameras that are positioned at different heights. It captures diverse settings of objects, background, context, sensor to scene distance, viewpoint angle and lighting conditions. The main purpose of OCID is to allow systematic comparison of existing object segmentation methods in scenes with increasing amount of clutter. In addition OCID does also provide ground-truth data for other vision tasks like object-classification and recognition.\r\n\r\nSource: [OCID](https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/object-clutter-indoor-dataset/)","description_withheld":null,"homepage":"https://www.acin.tuwien.ac.at/en/vision-for-robotics/software-tools/object-clutter-indoor-dataset/","introduced_date":"2019-02-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/easylabel-a-semi-automatic-pixel-wise-object","title":"EasyLabel: A Semi-Automatic Pixel-wise Object Annotation Tool for Creating Robotic RGB-D Datasets","first_author":"Markus Suchi","url":null},"license":null,"modalities":[{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Object Recognition","url":"/task/object-recognition","datasets_with_task":"/datasets/task/object-recognition"},{"name":"Unseen Object Instance Segmentation","url":"/task/unseen-object-instance-segmentation","datasets_with_task":"/datasets/task/unseen-object-instance-segmentation"}],"languages":[],"variants":["OCID"],"data_loaders":[],"num_papers_in_archive":29,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unseen-object-instance-segmentation-on-ocid","task":"Unseen Object Instance Segmentation","dataset_variant":"OCID","rows":1,"metrics":["Boundary F-measure"],"first_row_in_archive_order":{"model":"MSMFormer","paper":"/paper/mean-shift-mask-transformer-for-unseen-object","metrics":{"Boundary F-measure":"87.3"},"code_links":[{"title":"youngsean/unseenobjectswithmeanshift","url":"https://github.com/youngsean/unseenobjectswithmeanshift"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/mean-shift-mask-transformer-for-unseen-object","title":"Mean Shift Mask Transformer for Unseen Object Instance Segmentation","date":"2022-11-21","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"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."}