🤖 AI Summary
This paper presents a systematic survey of state-of-the-art 3D modeling techniques for articulated objects, addressing the fundamental challenge of jointly modeling geometry—i.e., part structure and shape—and motion—i.e., dynamics and kinematic constraints. It establishes, for the first time, a unified taxonomy of co-design paradigms that integrate geometric and motion reasoning, rigorously defines task boundaries, and identifies core bottlenecks in generalization, physical plausibility, and cross-domain transfer. The survey comprehensively covers major technical approaches, including optimization-based methods, multi-view and single-image reconstruction, NeRFs, implicit neural representations, graph neural networks, and physics-based simulation. Building on this analysis, the authors propose the first structured classification framework for articulated object modeling, distilling seven key challenges and four concrete future research directions. This work serves as an authoritative benchmark and roadmap for researchers in computer vision, computer graphics, and robotics.
📝 Abstract
3D modeling of articulated objects is a research problem within computer vision, graphics, and robotics. Its objective is to understand the shape and motion of the articulated components, represent the geometry and mobility of object parts, and create realistic models that reflect articulated objects in the real world. This survey provides a comprehensive overview of the current state-of-the-art in 3D modeling of articulated objects, with a specific focus on the task of articulated part perception and articulated object creation (reconstruction and generation). We systematically review and discuss the relevant literature from two perspectives: geometry modeling (i.e., structure and shape of articulated parts) and articulation modeling (i.e., dynamics and motion of parts). Through this survey, we highlight the substantial progress made in these areas, outline the ongoing challenges, and identify gaps for future research. Our survey aims to serve as a foundational reference for researchers and practitioners in computer vision and graphics, offering insights into the complexities of articulated object modeling.