# Re3Sim: Generating High-Fidelity Simulation Data via 3D-Photorealistic Real-to-Sim for Robotic Manipulation

arXiv: https://arxiv.org/abs/2502.08645 · HTML: https://mlsanity.com/paper/2502.08645/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2025.

## Built on by (most cited first, 30 of 43)

- [Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions](https://mlsanity.com/paper/2511.04665/) (2025)
- [Robot Learning from a Physical World Model](https://mlsanity.com/paper/2511.07416/) (2025)
- [GSWorld: Closed-Loop Photo-Realistic Simulation Suite for Robotic Manipulation](https://mlsanity.com/paper/2510.20813/) (2025)
- [RobotArena ∞: Scalable Robot Benchmarking via Real-to-Sim Translation](https://mlsanity.com/paper/2510.23571/) (2025)
- [ManipDreamer3D : Synthesizing Plausible Robotic Manipulation Video with Occupancy-aware 3D Trajectory](https://mlsanity.com/paper/2509.05314/) (2025)
- [Kinema4D: Kinematic 4D World Modeling for Spatiotemporal Embodied Simulation](https://mlsanity.com/paper/2603.16669/) (2026)
- [Manipulation as in Simulation: Enabling Accurate Geometry Perception in Robots](https://mlsanity.com/paper/2509.02530/) (2025)
- [AutoResearch AI: Towards AI-Powered Research Automation for Scientific Discovery](https://mlsanity.com/paper/2605.23204/) (2026)
- [SIMPACT: Simulation-Enabled Action Planning using Vision-Language Models](https://mlsanity.com/paper/2512.05955/) (2025)
- [Robot Learning from Any Images](https://mlsanity.com/paper/2509.22970/) (2025)
- [TwinAligner: Visual-Dynamic Alignment Empowers Physics-aware Real2Sim2Real for Robotic Manipulation](https://mlsanity.com/paper/2512.19390/) (2025)
- [Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations](https://mlsanity.com/paper/2604.27106/) (2026)
- [SimLauncher: Launching Sample-Efficient Real-world Robotic Reinforcement Learning via Simulation Pre-training](https://mlsanity.com/paper/2507.04452/) (2025)
- [Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface](https://mlsanity.com/paper/2512.19402/) (2025)
- [TTT-Parkour: Rapid Test-Time Training for Perceptive Robot Parkour](https://mlsanity.com/paper/2602.02331/) (2026)
- [Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents](https://mlsanity.com/paper/2610.02204/) (2026)
- [Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space](https://mlsanity.com/paper/2505.17389/) (2025)
- [TabletopGen: Tabletop Scene Generation and Interactive Simulation for Robotic Manipulation](https://mlsanity.com/paper/2512.01204/) (2025)
- [IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion](https://mlsanity.com/paper/2508.13153/) (2025)
- [Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)](https://mlsanity.com/paper/2509.06191/) (2025)
- [AOMGen: Photoreal, Physics-Consistent Demonstration Generation for Articulated Object Manipulation](https://mlsanity.com/paper/2512.18396/) (2025)
- [Splatwizard: A Benchmark Toolkit for 3D Gaussian Splatting Compression](https://mlsanity.com/paper/2512.24742/) (2025)
- [UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting](https://mlsanity.com/paper/2504.02158/) (2025)
- [DSG-World: Learning a 3D Gaussian World Model from Dual State Videos](https://mlsanity.com/paper/2506.05217/) (2025)
- [From Fields to Splats: A Cross-Domain Survey of Real-Time Neural Scene Representations](https://mlsanity.com/paper/2509.23555/) (2025)
- [CoEnv: Driving Embodied Multi-Agent Collaboration via Compositional Environment](https://mlsanity.com/paper/2604.05484/) (2026)
- [GASE: Gaussian Splatting-Based Automated System for Reconstructing Embodied-Simulation Environments](https://mlsanity.com/paper/2606.17520/) (2026)
- [ARSTAG: An Agentic Real2Sim2Real System for Task-Specific Robot Data Generation](https://mlsanity.com/paper/2609.24563/) (2026)
- [EMPIRIC: Experiment-Driven Learning of Residual World Models for Robot Planning](https://mlsanity.com/paper/2609.35047/) (2026)
- [Agentic RSR: Real-to-Sim-to-Real through Scene Reconstruction and Execution-Grounded Robot Policies](https://mlsanity.com/paper/2610.10479/) (2026)

## Abstract (arXiv)

Real-world data collection for robotics is costly and resource-intensive, requiring skilled operators and expensive hardware. Simulations offer a scalable alternative but often fail to achieve sim-to-real generalization due to geometric and visual gaps. To address these challenges, we propose a 3D-photorealistic real-to-sim system, namely, RE3SIM, addressing geometric and visual sim-to-real gaps. RE3SIM employs advanced 3D reconstruction and neural rendering techniques to faithfully recreate real-world scenarios, enabling real-time rendering of simulated cross-view cameras within a physics-based simulator. By utilizing privileged information to collect expert demonstrations efficiently in simulation, and train robot policies with imitation learning, we validate the effectiveness of the real-to-sim-to-real pipeline across various manipulation task scenarios. Notably, with only simulated data, we can achieve zero-shot sim-to-real transfer with an average success rate exceeding 58%. To push the limit of real-to-sim, we further generate a large-scale simulation dataset, demonstrating how a robust policy can be built from simulation data that generalizes across various objects. Codes and demos are available at: https://re3sim.github.io/.