发表机构
POSTECH（浦项科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对关节物体重建中参数纠缠导致的部件分解缺陷，提出StructureGS框架，结合结构感知引导的3D高斯溅射，通过定向包围盒约束实现更优重建性能。
AI 中文摘要
重建具有多个可移动部件的关节物体对于理解物体结构和实现物理交互至关重要。然而，该重建任务面临重大挑战，因为优化过程中几何、外观和运动参数相互纠缠。现有方法主要依赖光度监督，通常无法解开这些相互依赖的组件，导致部件分解效果差，边界模糊且存在几何伪影。为解决这一局限，我们提出StructureGS，一种关节物体重建框架，将结构感知引导融入3D高斯溅射。我们的方法利用物体部件的定向包围盒来强化两项关键结构属性：空间一致性，约束每个部件的几何在指定区域内保持紧凑且空间连贯；结构连通性，强制相邻部件间呈现物理上合理的接触关系。这些属性通过结构感知损失实现，将显式结构约束注入优化过程。大量实验表明，我们的方法在关节物体重建中达到了最先进的性能，生成具有清晰部件几何的高质量结果。
英文摘要
Reconstructing articulated objects with multiple movable parts is essential for understanding object structure and enabling physical interaction. However, this reconstruction task poses significant challenges due to the entanglement of geometry, appearance, and motion parameters during optimization. Existing methods rely primarily on photometric supervision, which commonly fails to disentangle these interdependent components, resulting in poor part decomposition with blurred boundaries and geometric artifacts. To address this limitation, we introduce StructureGS, a reconstruction framework for articulated objects that integrates structure-aware guidance into 3D Gaussian Splatting. Our approach leverages oriented bounding boxes of object parts to enforce two key structural properties: spatial coherence, which constrains each part's geometry to remain compact and spatially coherent within its designated region, and structural connectivity, which enforces physically plausible contact relationships between adjacent parts. These properties are realized through structure-aware losses that inject explicit structural constraints into the optimization process. Extensive experiments demonstrate that our method achieves state-of-the-art performance in articulated object reconstruction, producing high-quality results with well-defined part geometries.
Commentsaccepted at ECCV 2026
