Real-World 3D Data: Powering Real2Sim Simulation for Embodied AI Deployment
机器之心 · wechat · 2026-07-31
As the industrialization of embodied AI accelerates, Real2Sim simulation technology has become a crucial component. While systems like SimFoundry recently introduced by Fei-Fei Li's team validate the value of lightweight, video-driven simulation for rapid prototyping, Realsee proposes a different approach: constructing virtual worlds directly from real-world spatial data.
Leveraging a massive dataset of over 60 million real 3D spaces, Realsee has built a standardized Real2Sim pipeline. Compared to 2D video inference, this solution offers three core advantages:
- Millimeter-level Precision: Achieves strict 1:1 mapping of physical spaces using proprietary LiDAR equipment, eliminating scale ambiguity.
- Zero Cumulative Error: Maintains high geometric consistency, solving the trajectory drift issue common in traditional large-scale visual mapping.
- Multi-dimensional Data: Provides complete 3D structured data including depth, normals, materials, and exact object poses, avoiding cognitive bias from algorithmic hallucinations.
The article notes that while lightweight video simulation suits basic model iteration, native simulation based on real 3D data is a core necessity for supporting the industrial-grade deployment and generalization of embodied AI.
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