{"url":"/dataset/scenenn","name":"SceneNN","full_name":null,"description_markdown":"SceneNN is an RGB-D scene dataset consisting of more than 100 indoor scenes. The scenes are captured at various places, e.g., offices, dormitory, classrooms, pantry, etc., from University of Massachusetts Boston and Singapore University of Technology and Design.\r\nAll scenes are reconstructed into triangle meshes and have per-vertex and per-pixel annotation. The dataset is additionally enriched with fine-grained information such as axis-aligned bounding boxes, oriented bounding boxes, and object poses.\r\n\r\nSource: [SceneNN: A Scene Meshes Dataset with aNNotations](http://103.24.77.34/scenenn/home/)\r\nImage Source: [http://103.24.77.34/scenenn/home/](http://103.24.77.34/scenenn/home/)","description_withheld":null,"homepage":"http://103.24.77.34/scenenn/home/","introduced_date":"2016-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"SceneNN: A Scene Meshes Dataset with aNNotations","first_author":null,"url":"https://doi.org/10.1109/3DV.2016.18"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"3D Instance Segmentation","url":"/task/3d-instance-segmentation-1","datasets_with_task":"/datasets/task/3d-instance-segmentation-1"}],"languages":[],"variants":["SceneNN"],"data_loaders":[],"num_papers_in_archive":63,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-instance-segmentation-on-scenenn-1","task":"3D Instance Segmentation","dataset_variant":"SceneNN","rows":3,"metrics":["mAP@0.5"],"first_row_in_archive_order":{"model":"OccuSeg","paper":"/paper/occuseg-occupancy-aware-3d-instance","metrics":{"mAP@0.5":"47.1"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/occuseg-occupancy-aware-3d-instance","title":"OccuSeg: Occupancy-aware 3D Instance Segmentation","date":"2020-03-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/jsis3d-joint-semantic-instance-segmentation","title":"JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields","date":"2019-04-01","rows_on_this_dataset":2,"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."}