无需3D标注，自监督重建可动物体的形貌与运动参数
ArticulatedGS: Self-supervised Digital Twin Modeling of Articulated Objects using 3D Gaussian Splatting
- 分步优化解耦复杂参数，实现稳定高质重建
- 在部件分割、运动估计和视觉质量上均优于现有方法
- 适合构建无需人工标注的可动物体数字孪生
本文针对可动物体数字孪生建模中同时进行部件级三维重建、外观建模与运动参数估计的挑战，提出基于3D高斯点阵（3D-GS）的方法。利用两组分别展示物体不同静态姿态的多视角图像，实现外观与几何信息同步重建。通过多步优化过程解耦高度耦合的参数，避免依赖3D标注、运动信号或语义标签，实现自监督学习。实验表明，该方法在部件分割准确率、运动估计精度和视觉质量方面均达到同类方法最优表现。
原文摘要 · Abstract (English)
We tackle the challenge of concurrent reconstruction at the part level with the RGB appearance and estimation of motion parameters for building digital twins of articulated objects using the 3D Gaussian Splatting (3D-GS) method. With two distinct sets of multi-view imagery, each depicting an object in separate static articulation configurations, we reconstruct the articulated object in 3D Gaussian representations with both appearance and geometry information at the same time. Our approach decoupled multiple highly interdependent parameters through a multi-step optimization process, thereby achieving a stable optimization procedure and high-quality outcomes. We introduce ArticulatedGS, a self-supervised, comprehensive framework that autonomously learns to model shapes and appearances at the part level and synchronizes the optimization of motion parameters, all without reliance on 3D supervision, motion cues, or semantic labels. Our experimental results demonstrate that, among comparable methodologies, our approach has achieved optimal outcomes in terms of part segmentation accuracy, motion estimation accuracy, and visual quality.
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