Part$^{2}$GS: 基于3D高斯溅射的关节物体部分感知建模
Part$^{2}$GS: Part-aware Modeling of Articulated Objects using 3D Gaussian Splatting
AI总结：
本文提出Part$^{2}$GS框架，通过部分感知的3D高斯表示实现高保真度的多部件关节物体建模，结合物理一致的运动约束，提升运动连贯性。
摘要：
关节物体在现实世界中普遍存在，但其结构和运动建模仍是3D重建方法的挑战。本文提出Part$^{2}$GS，一种新型框架，用于高保真度建模多部件关节数字双。Part$^{2}$GS利用部分感知的3D高斯表示，编码关节组件并学习可训练属性，实现结构化、解耦的变换，保持高保真几何。为确保物理一致的运动，我们提出一种运动感知的规范表示，由基于物理的约束引导，包括接触强制、速度一致性及矢量场对齐。此外，我们引入一个斥力点场以防止部件碰撞并维持稳定的关节路径，显著提高了运动连贯性。在合成和真实数据集上的广泛评估表明，Part$^{2}$GS在可移动部件的Chamfer距离上比最先进方法高出多达10倍。
英文摘要：
Articulated objects are common in the real world, yet modeling their structure and motion remains a challenging task for 3D reconstruction methods. In this work, we introduce Part$^{2}$GS, a novel framework for modeling articulated digital twins of multi-part objects with high-fidelity geometry and physically consistent articulation. Part$^{2}$GS leverages a part-aware 3D Gaussian representation that encodes articulated components with learnable attributes, enabling structured, disentangled transformations that preserve high-fidelity geometry. To ensure physically consistent motion, we propose a motion-aware canonical representation guided by physics-based constraints, including contact enforcement, velocity consistency, and vector-field alignment. Furthermore, we introduce a field of repel points to prevent part collisions and maintain stable articulation paths, significantly improving motion coherence over baselines. Extensive evaluations on both synthetic and real-world datasets show that Part$^{2}$GS consistently outperforms state-of-the-art methods by up to 10$\times$ in Chamfer Distance for movable parts.
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。
