通过自适应融合运动假设，提升复杂可动物体重建的精度与稳定性。
ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposals
- 基于几何特征和运动先验进行过分割初始化，生成合理运动猜想。
- 动态合并空间邻近部分，碰撞感知剪枝避免错误运动估计。
- 适用于机器人操作与交互模拟，对复杂多部件物体效果显著。
将可动物体重建为高保真数字孪生体对于机器人操作和交互仿真至关重要。现有基于可微渲染框架（如3D高斯泼溅）的自监督方法仍高度依赖初始部件分割，其对启发式聚类或预训练模型的依赖常导致优化陷入局部极小值，尤其在复杂多部件物体上表现不佳。为此，我们提出ArtPro，一种引入运动假设自适应融合的新自监督框架。方法始于由几何特征与运动先验引导的过分割初始化，生成具有合理运动假设的部件提案。优化过程中，通过分析空间邻近部件间的运动一致性动态合并提案，并引入碰撞感知运动剪枝机制，防止错误运动估计。在合成与真实世界物体上的大量实验表明，ArtPro在准确性和稳定性上显著优于现有方法，实现了复杂多部件物体的鲁棒重建。
原文摘要 · Abstract (English)
Reconstructing articulated objects into high-fidelity digital twins is crucial for applications such as robotic manipulation and interactive simulation. Recent self-supervised methods using differentiable rendering frameworks like 3D Gaussian Splatting remain highly sensitive to the initial part segmentation. Their reliance on heuristic clustering or pre-trained models often causes optimization to converge to local minima, especially for complex multi-part objects. To address these limitations, we propose ArtPro, a novel self-supervised framework that introduces adaptive integration of mobility proposals. Our approach begins with an over-segmentation initialization guided by geometry features and motion priors, generating part proposals with plausible motion hypotheses. During optimization, we dynamically merge these proposals by analyzing motion consistency among spatial neighbors, while a collision-aware motion pruning mechanism prevents erroneous kinematic estimation. Extensive experiments on both synthetic and real-world objects demonstrate that ArtPro achieves robust reconstruction of complex multi-part objects, significantly outperforming existing methods in accuracy and stability.
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