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Survey on Modeling of Human-made Articulated Objects

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arxiv 2403.14937 v3 pith:K4PC23T6 submitted 2024-03-22 cs.CV

Survey on Modeling of Human-made Articulated Objects

classification cs.CV
keywords articulatedmodelingobjectssurveyobjectpartscomputergeometry
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Functionalization via Structure Completion and Motion Rectification

    cs.CV 2026-05 unverdicted novelty 7.0

    Object functionalization is cast as neural graph completion over a functional graph of parts, contacts, and motions, followed by geometry realization that also rectifies erroneous motions, demonstrated on furniture wi...

  2. QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation

    cs.RO 2026-04 unverdicted novelty 6.0

    QDTraj uses Quality-Diversity algorithms with sparse rewards to produce at least five times more diverse high-performing trajectories for articulated object manipulation than compared methods, validated across 30 obje...

  3. FunRec: Reconstructing Functional 3D Scenes from Egocentric Interaction Videos

    cs.CV 2026-04 unverdicted novelty 6.0

    FunRec reconstructs interactable 3D scenes with articulated parts from in-the-wild egocentric interaction videos, automatically discovering parts, estimating kinematics, and producing simulation-compatible meshes with...