{"url":"/dataset/fraunhofer-ezrt-xxl-ct-instance-segmentation","name":"Fraunhofer EZRT XXL-CT Instance Segmentation Me163","full_name":null,"description_markdown":"The 'Me 163' was a Second World War fighter airplane and a result of the German air force secret developments. One of these airplanes is currently owned and displayed in the historic aircraft exhibition of the 'Deutsches Museum' in Munich, Germany. To gain insights with respect to its history, design and state of preservation, a complete CT scan was obtained using an industrial XXL-computer tomography scanner at Fraunhofer EZRT .\r\n\t\t\r\nUsing the CT data from the Me 163, all its details can visually be examined at various levels, ranging from the complete hull down to single sprockets and rivets. However, while a trained human observer can identify and interpret the volumetric data with all its parts and connections, a virtual dissection of the airplane and all its different parts would be quite desirable. Nevertheless, this means, that an instance segmentation of all components and objects of interest into disjoint entities from the CT data is necessary. \t\t\r\n\t\t\r\nAs of currently, no adequate computer-assisted tools for automated or semi-automated segmentation of such XXL-airplane data are available, in a first step, an interactive data annotation and object labelling process has been established. So far, seven $512 \\times 512 \\times 512$ voxel sub-volumes from the Me 163 airplane have been annotated and labelled, whose results can potentially be used for various new applications in the field of digital heritage, non-destructive testing, or machine-learning.","description_withheld":null,"homepage":"https://zenodo.org/records/10651746","introduced_date":"2022-12-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/an-annotated-instance-segmentation-xxl-ct","title":"An annotated instance segmentation XXL-CT data-set from a historic airplane","first_author":"Roland Gruber","url":null},"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"3D Instance Segmentation","url":"/task/3d-instance-segmentation-1","datasets_with_task":"/datasets/task/3d-instance-segmentation-1"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Fraunhofer EZRT XXL-CT Instance Segmentation Me163"],"data_loaders":[],"num_papers_in_archive":1,"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."}