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3D Instance Segmentation datasets
archive 2025-07-28
18 datasets carry the task tag "3D Instance Segmentation" (the task itself: 3D Instance Segmentation), ordered by the archive's paper count. Page 1 of 1: 18 shown of 18. Facet routes are this site's own (the archive records the tag string, not a page).
The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.
Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets
3D Instance Segmentation datasets 1–18 of 18
ScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data.
1,595 papers · 21 benchmarks
S3DIS (Stanford 3D Indoor Scene Dataset (S3DIS))
The Stanford 3D Indoor Scene Dataset (S3DIS) dataset contains 6 large-scale indoor areas with 271 rooms.
488 papers · 9 benchmarks
KITTI-360 is a large-scale dataset that contains rich sensory information and full annotations.
246 papers · 7 benchmarks
PartNet is a consistent, large-scale dataset of 3D objects annotated with fine-grained, instance-level, and hierarchical 3D part information.
156 papers · 3 benchmarks
SceneNN is an RGB-D scene dataset consisting of more than 100 indoor scenes.
63 papers · 1 benchmark
The ScanNet200 benchmark studies 200-class 3D semantic segmentation - an order of magnitude more class categories than previous 3D scene understanding benchmarks.
45 papers · 3 benchmarks
Our project (STPLS3D) aims to provide a large-scale aerial photogrammetry dataset with synthetic and real annotated 3D point clouds for semantic and instance segmentation tasks.
36 papers · 3 benchmarks
ScanNet++ (ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes)
ScanNet++ is a large scale dataset with 450+ 3D indoor scenes containing sub-millimeter resolution laser scans, registered 33-megapixel DSLR images, and commodity RGB-D streams from iPhone.
25 papers · 5 benchmarks
FOR-instance (FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees)
The challenge of accurately segmenting individual trees from laser scanning data hinders the assessment of crucial tree parameters necessary for effective forest management, impacting many downstream applications.
7 papers · 0 benchmarks
MICCAI Challenge on Circuit Reconstruction from Electron Microscopy Images.
4 papers · 1 benchmark
We introduce MultiScan, a scalable RGBD dataset construction pipeline leveraging commodity mobile devices to scan indoor scenes with articulated objects and web-based semantic annotation interfaces to efficiently annotate object and part…
4 papers · 1 benchmark
Contains mitochondria instances.
3 papers · 1 benchmark
LoTE-Animal (LoTE-Animal: A Long Time-span Dataset for Endangered Animal Behavior Understanding)
Understanding and analyzing animal behavior is increasingly essential to protect endangered animal species.
2 papers · 1 benchmark
The 'Me 163' was a Second World War fighter airplane and a result of the German air force secret developments.
1 paper · 0 benchmarks
Heritage Pointcloud Instance Collection dataset, acquired from two large buildings and annotated at a point-wise semantic level based on existent BIM models.
1 paper · 1 benchmark
XA Bin-Picking is a point-cloud dataset comprising both simulated and real-world scenes with three industrial parts.
1 paper · 0 benchmarks
InLUT3D (Indoor Lodz University of Technology Point Cloud Dataset)
This dataset called Indoor Lodz University of Technology Point Cloud Dataset (InLUT3D) is a point cloud set tailored for real object classification and both semantic and instance segmentation tasks.
0 papers · 0 benchmarks
InfiniteRep is a synthetic, open-source dataset for fitness and physical therapy (PT) applications.
0 papers · 0 benchmarks
Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.