DATASET FACET · MODALITY
Point cloud datasets for physical AI
3D points from depth cameras, LiDAR, reconstruction, or simulation, used for scene geometry, mapping, object shape, and manipulation planning.
DIRECT ANSWER
Point cloud pages collect datasets where this modalityis materially relevant, then add truelabel’s commercial use, consent risk, and deployment fit notes so buyers can decide whether public data is enough.
MATCHED DATASETS
6 catalog entries
6 of 6 datasets
ManiSkill
Published May 2026 · apache-2
A simulation benchmark and toolkit for manipulation skills and embodied AI policy evaluation.
- Simulation suite with tasks and environments maintained by the ManiSkill project.
- Source appears permissive; verify data terms
- Best for: simulated manipulation
ScanNet
Published May 2026 · custom
An indoor RGB-D reconstruction dataset used for 3D scene understanding.
- Large indoor RGB-D scene reconstruction corpus.
- Commercial use restricted
- Best for: 3D scene understanding
Habitat datasets
Published May 2026 · custom
A family of embodied AI datasets and simulation assets for navigation and rearrangement research.
- Multiple scene and task datasets under the AI Habitat ecosystem.
- Commercial use unclear
- Best for: embodied navigation
Waymo Open Dataset
Published May 2026 · custom
A large autonomous driving dataset with camera, LiDAR, and labeled traffic scenes.
- Large autonomous driving scenes with cameras and LiDAR.
- Commercial use restricted
- Best for: autonomous driving perception
ObjectFolder
Published May 2026 · custom
A dataset family for object-centric physical properties, geometry, and multimodal perception research.
- Object-centric multimodal assets; source materials define current object count and modalities.
- Commercial use unclear
- Best for: object perception
RoboCasa
Published May 2026 · custom
A large-scale kitchen simulation framework and dataset family for everyday manipulation tasks in diverse household environments.
- RoboCasa365 source materials describe 365 everyday tasks, 2,500 kitchen environments, 600+ hours of human demonstration data, and 1,600+ hours of synthetic demonstrations.
- Commercial use unclear
- Best for: large-scale kitchen simulation
READ THE TAG WITH CARE
Do not treat this tag as the whole sourcing decision
Facet groupings are discovery aids, not final recommendations. A shared modality, task, robot, format, license, or commercial-use label only says that datasets are worth comparing; it does not prove that the source is safe, complete, or useful for a target model.
Use this grouping to shortlist candidates, then open the dataset profiles, run fit and license checks, and compare sources against the buyer's target environment. Thin tag results become useful only when they route the reader into deeper evidence and action surfaces.
The external references below keep the facet grounded in robotics data practice. They help reviewers understand why format, embodiment, trajectory quality, licensing, and real-world coverage matter before a team commits engineering time to ingestion.
When a facet has only a few matching datasets, treat that as a signal rather than a weakness. It may mean the public corpus is thin for that robot, task, or format, and the next move is a custom supplement with the facet written into acceptance criteria.
Where to go next
- Physical AI dataset catalogUse the catalog to compare source-backed dataset profiles by modality, task, rights signal, consent risk, and deployment fit.
- Hugging Face robotics indexScan the broader robotics dataset surface before narrowing into promoted profiles, comparisons, and custom collection specs.
- Dataset changelogTrack source updates, licensing notes, and buyer-readiness changes that should trigger a renewed review.
- Dataset fit checkerScore whether a public source is enough for the model, rights path, modalities, and target environment.
- License risk checkerSeparate source license language from contributor consent, redistribution, private-space risk, and model-use assumptions.
- Data spec generatorTurn a public-source gap into a scoped capture request with sample QA, metadata, and delivery requirements.
- Vendor alternatives hubCompare data providers when the answer is not another public dataset but a better sourcing or capture route.
- Data annotation companiesUse the company index to separate annotation vendors, data engines, marketplaces, and specialist capture teams.
Other places to verify the claims
- Scale AI physical AI data engineMarket context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
- LeRobot documentationRobotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
- Open X-EmbodimentA cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
- DROID datasetA large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.
TRUELABEL ROUTING
Need point cloud data with cleaner rights?
Commission target-scoped data with contributor consent, capture metadata, sample QA, and delivery into your own storage.