Coohom Cloud provides a synthetic data platform that generates large‑scale, annotated indoor datasets—including 2D images, videos, 3D models, and point clouds—using a library of floor plans and interior assets. The service delivers physically realistic scenes with detailed semantic, material, and state annotations, compatible with robot simulation frameworks, AIGC training pipelines, XR content creation, and product visualization, while ensuring privacy‑compliant data without real‑world personal information.
Funding
Funding not disclosed
Founders
Product
Problem
Robotic and indoor smart device developers struggle to obtain large, high‑quality indoor datasets for mapping, navigation, object recognition, and simulation, while generating such data manually is costly, time‑consuming, and raises privacy concerns.
Solution
Coohom Cloud offers a synthetic data platform that generates annotated 2D images, videos, 3D models, and full indoor scenes at scale. Leveraging a massive library of floor plans and interior assets, the service creates physically realistic environments with configurable lighting, material properties, and dynamic elements. Automated pipelines combine AI‑driven scene synthesis with manual quality checks to produce ready‑to‑use datasets that include semantic, material, and state annotations. The data are delivered without any real‑world personal information, ensuring compliance with privacy regulations. Customers can access the datasets via download or API and request custom data tailored to specific robot platforms, AIGC training, XR content creation, or product visualization needs.
Target Audience
Primary customers are manufacturers of indoor robots (e.g., vacuum cleaners, care robots, drones), AIGC research teams, XR content creators, and enterprises needing high‑quality visual product promotion assets.
Features
- High‑fidelity synthetic indoor scenes generated from a vast catalog of floor plans, furniture, and appliances
- Physical property enrichment (density, friction, elasticity, damping) and support for movable parts such as doors and drawers
- Multi‑modal outputs: 2D images, video sequences, 3D models, point clouds, and annotated scene metadata
- Comprehensive labeling covering semantics, materials, object states, and spatial relationships, produced by combined automated and manual processes
- Compatibility with major simulation frameworks (Gazebo, Unreal Engine, Isaac Sim) via ready‑to‑import data formats
- Custom data pipelines that allow parameterized camera trajectories, lighting conditions, and scene variations
- Built‑in data security measures that exclude any real personal data, ensuring regulatory compliance