用3D高斯点云快速构建可交互的高保真数字孪生，提升机器人操作可靠性。
A High-Fidelity Digital Twin for Robotic Manipulation Based on 3D Gaussian Splatting
- 基于3D高斯点云实现分钟级高保真场景重建。
- 融合语义信息与过滤算法，生成可用于运动规划的碰撞模型。
- 在Franka机械臂上实测成功，适合复杂环境下的机器人控制研究。
构建高保真、可交互的数字孪生对实现闭环运动规划和可靠的现实机器人执行至关重要，是推动模拟到现实迁移的关键。然而，现有方法常面临重建速度慢、视觉保真度低以及将照片级模型转换为可用碰撞几何困难等问题。本文提出一种实用框架，仅需稀疏RGB输入即可在数分钟内构建高质量数字孪生。系统采用3D高斯点云（3DGS）实现快速、逼真的场景重建，并引入可见性感知的语义融合以实现精确三维标注，同时设计一种高效的滤波式几何转换方法，生成可无缝集成至Unity-ROS2-MoveIt物理引擎的碰撞模型。在Franka Emika Panda机械臂执行抓取放置任务的实验中，验证了增强后的几何精度能有效支持真实世界中的鲁棒操作。结果表明，结合语义与几何一致性的3DGS数字孪生，为非结构化环境中从感知到操作提供了快速、可靠且可扩展的路径。
原文摘要 · Abstract (English)
Developing high-fidelity, interactive digital twins is crucial for enabling closed-loop motion planning and reliable real-world robot execution, which are essential to advancing sim-to-real transfer. However, existing approaches often suffer from slow reconstruction, limited visual fidelity, and difficulties in converting photorealistic models into planning-ready collision geometry. We present a practical framework that constructs high-quality digital twins within minutes from sparse RGB inputs. Our system employs 3D Gaussian Splatting (3DGS) for fast, photorealistic reconstruction as a unified scene representation. We enhance 3DGS with visibility-aware semantic fusion for accurate 3D labelling and introduce an efficient, filter-based geometry conversion method to produce collision-ready models seamlessly integrated with a Unity-ROS2-MoveIt physics engine. In experiments with a Franka Emika Panda robot performing pick-and-place tasks, we demonstrate that this enhanced geometric accuracy effectively supports robust manipulation in real-world trials. These results demonstrate that 3DGS-based digital twins, enriched with semantic and geometric consistency, offer a fast, reliable, and scalable path from perception to manipulation in unstructured environments.
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