ArtPro：自适应整合可动性提案的自监督关节物体重建
ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposals
把可动部件分割当作可优化的过分割提案，在可微渲染优化中按空间邻域运动一致性动态合并，并用碰撞感知的运动剪枝避免错误运动学估计；这套「提案+动态合并」思路可直接迁移到基于 3DGS 的关节物体建模、部件级场景分解与机器人交互仿真。
原文依据 we dynamically merge these proposals by analyzing motion consistency among spatial neighbors
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论将关节物体重建为高保真数字孪生，对机器人操作与交互式仿真等应用至关重要。近期使用 3D 高斯泼溅等可微渲染框架的自监督方法仍对初始部件分割高度敏感。它们依赖启发式聚类或预训练模型，往往导致优化收敛到局部极小值，对复杂多部件物体尤为如此。为解决这些局限，我们提出 ArtPro，一种引入可动性提案自适应整合的新型自监督框架。该方法首先进行由几何特征与运动先验引导的过分割初始化，生成带有合理运动假设的部件提案。在优化过程中，我们通过分析空间邻域间的运动一致性来动态合并这些提案，同时碰撞感知的运动剪枝机制可防止错误的运动学估计。在合成与真实物体上的大量实验表明，ArtPro 能够稳健地重建复杂多部件物体，在精度与稳定性上显著优于现有方法。
英文原文摘要 1205 字符
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.