GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments
Training embodied agents in the real world requires skilled operators and expensive hardware. Simulation environments offer a compelling alternative by enabling large-scale, cost-effective data augmentation. Consequently, rapidly constructing high-fidelity simulation scenes with a minimal sim-to-real gap has become a critical objective in robot learning. While reconstruction-based methods provide superior visual quality, current workflows are hindered by inefficient data acquisition and subpar foreground object extraction. We thus propose GASE, a highly automated system for simulation scene construction. GASE leverages multi-view video streams from panoramic camera arrays to enable rapid ...
| Authors | Jiawei Zhang, Yiming Yan, Chao Liang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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