Collectiv builds the Deep World Model, a continuously updated digital twin of the physical world. This AI-driven platform uses a community-powered network to generate dynamic, photorealistic 3D models from minimal data, enabling advanced spatial intelligence for AI, robotics, and immersive experiences.
Funding
Funding not disclosed
Founders
Product
Problem
Current spatial data solutions are static, outdated, or incomplete, creating a bottleneck for advancements in AI, robotics, and immersive experiences that require real-time environmental understanding. This limitation hinders the development of applications demanding dynamic, context-aware spatial intelligence.
Solution
Collectiv is developing the Deep World Model (DWM), a living digital twin of the physical world. This model is continuously updated by the community-powered Beetle data network, providing a dynamic and context-aware foundation for spatial intelligence. The DWM leverages proprietary AI, specifically Signal-Guided Reconstruction technology, to create photorealistic, dynamic 3D models from minimal visual data and ambient signals. This approach enables the generation of multiple 3D representations, including point clouds, meshes, and Gaussian Splatting, tailored for specific industry needs. The platform is designed for global scalability and offers unmatched data efficiency, making it an ideal simulation environment for AI agents.
Target Audience
Primary customers include developers and organizations in robotics, drone operations, urban planning, smart cities, gaming, and entertainment seeking real-time, dynamic 3D spatial data for advanced AI applications and immersive experiences.
Features
- Deep World Model (DWM): A continuously updated, AI-driven digital twin of the physical world.
- Signal-Guided Reconstruction: Proprietary AI technology for generating 3D models from minimal visual and ambient signal data.
- Multiple 3D Representation Outputs: Generates point clouds, polygonal meshes, and Gaussian Splatting models for diverse application requirements.
- Real-Time Data Ingestion: Seamlessly integrates new data from the Beetle network to evolve and update the digital twin.
- Beetle Data Network: A decentralized network utilizing smartphones and IoT devices for crowdsourced data collection.
- DePIN Framework: Leverages a Decentralized Physical Infrastructure Network for data sourcing and incentivization.
- Dual-Data Capture: Collects both visual data (photos, videos) and ambient signals (Wi-Fi, Bluetooth, GPS) for enhanced model accuracy.
- On-Chain Data Ownership: Digital assets representing data contribution streams with dynamic metadata and monetizable access.
- Project Unfold: An open-source initiative to build the world's largest indoor dataset for Spatial AI research.