GEAR: 基于高斯点划法的几何-运动交替优化用于关节物体建模
GEAR: GEometry-motion Alternating Refinement for Articulated Object Modeling with Gaussian Splatting
AI总结：
GEAR通过交替优化几何与运动，提升关节物体重建的精度和泛化能力，尤其在复杂多关节物体上表现优异。
摘要：
高保真交互数字资产对具身智能和机器人交互至关重要，但关节物体因复杂结构和耦合的几何-运动关系仍难以重建。现有方法在几何-运动联合优化中不稳定，且在复杂多关节或分布外物体上泛化能力有限。为此，我们提出GEAR，一种EM风格的交替优化框架，将几何和运动作为相互依赖的组件联合建模于高斯点划法表示中。GEAR将部分分割视为潜在变量，将关节运动参数作为显式变量，交替优化以提高收敛性和几何-运动一致性。为提升部分分割质量而不牺牲泛化能力，我们利用普通的2D分割模型提供多视图部分先验，并采用弱监督约束来正则化潜在变量。在多个基准和新构建的GEAR-Multi数据集上实验表明，GEAR在几何重建和运动参数估计上取得了最先进的结果，尤其在具有多个可移动部分的复杂关节物体上表现突出。
英文摘要：
High-fidelity interactive digital assets are essential for embodied intelligence and robotic interaction, yet articulated objects remain challenging to reconstruct due to their complex structures and coupled geometry-motion relationships. Existing methods suffer from instability in geometry-motion joint optimization, while their generalization remains limited on complex multi-joint or out-of-distribution objects. To address these challenges, we propose GEAR, an EM-style alternating optimization framework that jointly models geometry and motion as interdependent components within a Gaussian Splatting representation. GEAR treats part segmentation as a latent variable and joint motion parameters as explicit variables, alternately refining them for improved convergence and geometric-motion consistency. To enhance part segmentation quality without sacrificing generalization, we leverage a vanilla 2D segmentation model to provide multi-view part priors, and employ a weakly supervised constraint to regularize the latent variable. Experiments on multiple benchmarks and our newly constructed dataset GEAR-Multi demonstrate that GEAR achieves state-of-the-art results in geometric reconstruction and motion parameters estimation, particularly on complex articulated objects with multiple movable parts.
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