The increasing demand for augmented reality and robotics is driving the need for articulated object reconstruction with high scalability. However, existing settings for reconstructing from discrete articulation states or casual monocular videos require non-trivial axis alignment or suffer from insufficient coverage, limiting their applicability. In this paper, we introduce FreeArtGS, a novel method for reconstructing articulated objects under free-moving scenario, a new setting with a simple setup and high scalability. FreeArtGS combines free-moving part segmentation with joint estimation and end-to-end optimization, taking only a monocular RGB-D video as input. By optimizing with the priors from off-the-shelf point-tracking and feature models, the free-moving part segmentation module identifies rigid parts from relative motion under unconstrained capture. The joint estimation module calibrates the unified object-to-camera poses and recovers joint type and axis robustly from part segmentation. Finally, 3DGS-based end-to-end optimization is implemented to jointly reconstruct visual textures, geometry, and joint angles of the articulated object. We conduct experiments on two benchmarks and real-world free-moving articulated objects. Experimental results demonstrate that FreeArtGS consistently excels in reconstructing free-moving articulated objects and remains highly competitive in previous reconstruction settings, proving itself a practical and effective solution for realistic asset generation. The project page is available at: https://freeartgs.github.io/

翻译：增强现实与机器人技术的日益增长需求，驱动着对高可扩展性铰接式物体重建的探索。然而，现有基于离散铰接状态或随意单目视频的重建设定，要么需要繁琐的轴向对齐，要么因覆盖范围不足而受限，影响了其实际应用。本文提出FreeArtGS——一种在自由运动场景下重建铰接式物体的新方法，该方法采用简单设定并具备高可扩展性。FreeArtGS融合了自由运动部件分割、关节估计与端到端优化，仅需输入单目RGB-D视频。通过利用现有点跟踪与特征模型的先验知识进行优化，自由运动部件分割模块可在无约束捕捉条件下从相对运动中识别刚性部件；关节估计模块则校准物体到相机的统一位姿，并基于部件分割结果稳健地恢复关节类型与轴线。最终，基于3DGS的端到端优化被用于联合重建铰接式物体的视觉纹理、几何结构及关节角度。我们在两个基准数据集及真实自由运动铰接式物体上开展了实验。结果表明，FreeArtGS在自由运动铰接式物体重建中持续表现优异，同时在以往重建设定中仍具备强竞争力，证明了其作为实际资产生成方案的有效性。项目页面见：https://freeartgs.github.io/