ArtiTwinSplat：基于RGB-D视频的高斯泼溅实现可交互数字孪生重建
ArtiTwinSplat: Interactable Digital Twin Reconstruction via Gaussian Splatting from RGB-D videos
AI总结：
提出ArtiTwinSplat框架，利用3D高斯泼溅从RGB-D视频自动构建可交互的铰接物体数字孪生，无需CAD模型或人工标注，支持实时渲染与交互操作。
摘要：
在非结构化真实环境中部署机器人需要准确、可交互的物体模型。大规模构建这些模型仍是机器人系统集成的关键瓶颈。我们提出ArtiTwinSplat，一个直接从RGB-D视频自动构建铰接、逼真的物体数字孪生的框架，无需CAD模型、仿真资产或手动标注。我们的方法基于3D高斯泼溅，保持几何保真度和光度真实性，并结合无监督铰接发现流程，仅从观察到的运动恢复部件结构和关节运动学。通过跟踪和优化阶段，我们的方法提供稳定、可查询的数字孪生，支持实时渲染、视角控制和交互操作。与局限于仿真的先前方法不同，ArtiTwinSplat直接处理真实世界观测，并生成可立即用于下游机器人规划和学习系统的孪生体。该方法为数字孪生构建提供了一条实用、可扩展的路径，降低了在具身AI和人机协作背景下铰接物体操作的集成门槛。
英文摘要：
Deploying robots in unstructured real-world environments needs accurate, interactive models of the objects. Constructing these models at scale remains a critical bottleneck for robotic system integration. We present ArtiTwinSplat, a framework that automatically constructs articulated, photo-realistic digital twins of objects directly from RGB-D videos, requiring no CAD models, simulation assets, or manual annotations. Our method is built on 3D Gaussian Splatting that preserve geometric fidelity and photometric realism, coupled with an unsupervised articulation discovery pipeline that recovers part structure and joint kinematics from observed motion alone. With tracking and optimization stages our method provides stable, queryable digital twins that support real-time rendering, viewpoint control, and interactive manipulation. Unlike prior methods confined to simulation, ArtiTwinSplat operates directly on real-world observations and produces twins that are immediately usable by downstream robot planning and learning systems. This method offers a practical, scalable pathway toward digital twin construction, lowering the integration barrier for articulated object manipulation in embodied AI and human-robot collaboration contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
