{"url":"/dataset/box-is","name":"Box-IS","full_name":"Box-IS","description_markdown":"RGB-D instance segmentation box dataset. The Box-IS dataset was created to support research on human-robot collaboration with a focus on robotic manipulation tasks. It was captured using the Intel® RealSense™ Depth Camera D455, a high-performance sensor designed for depth imaging. To ensure precise depth measurements, we bypassed the default depth data processing of the sensor and performed accurate stereo matching directly from the captured left and right IR images. Employing the UniMatch technique, we derived a high-quality depth map from these stereo IR images, which was then aligned with the corresponding RGB image for a comprehensive output. The dataset was intentionally designed to encompass a broad range of scene complexities, from simple box arrangements to highly irregular configurations. This diversity ensures that it can effectively benchmark algorithms across varying levels of difficulty.","description_withheld":null,"homepage":"https://github.com/AIM-SKKU/Box-IS","introduced_date":"2025-01-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/iam-enhancing-rgb-d-instance-segmentation","title":"IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks","first_author":"Aecheon Jung","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Segmentation","url":"/task/segmentation","datasets_with_task":"/datasets/task/segmentation"},{"name":"RGB-D Instance Segmentation","url":"/task/rgb-d-instance-segmentation","datasets_with_task":"/datasets/task/rgb-d-instance-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Box-IS"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/instance-segmentation-on-box-is","task":"Instance Segmentation","dataset_variant":"Box-IS","rows":1,"metrics":["mask AP"],"first_row_in_archive_order":{"model":"IAM + SOLQ","paper":"/paper/iam-enhancing-rgb-d-instance-segmentation","metrics":{"mask AP":"83.7"},"code_links":[{"title":"aim-skku/sun-rgbd-is","url":"https://github.com/aim-skku/sun-rgbd-is"},{"title":"aim-skku/nyudv2-is","url":"https://github.com/aim-skku/nyudv2-is"},{"title":"aim-skku/box-is","url":"https://github.com/aim-skku/box-is"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/rgb-d-instance-segmentation-on-box-is","task":"RGB-D Instance Segmentation","dataset_variant":"Box-IS","rows":1,"metrics":["mask AP"],"first_row_in_archive_order":{"model":"IAM + SOLQ","paper":"/paper/iam-enhancing-rgb-d-instance-segmentation","metrics":{"mask AP":"83.7"},"code_links":[{"title":"aim-skku/sun-rgbd-is","url":"https://github.com/aim-skku/sun-rgbd-is"},{"title":"aim-skku/nyudv2-is","url":"https://github.com/aim-skku/nyudv2-is"},{"title":"aim-skku/box-is","url":"https://github.com/aim-skku/box-is"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/iam-enhancing-rgb-d-instance-segmentation","title":"IAM: Enhancing RGB-D Instance Segmentation with New Benchmarks","date":"2025-01-03","rows_on_this_dataset":2,"code_links":3,"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."}