Category-Level Articulated Object Pose Estimation
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This paper addresses the task of category-level pose estimation for articulated objects from a single depth image. We present a novel category-level approach that correctly accommodates object instances previously unseen during training. We introduce Articulation-aware Normalized Coordinate Space Hierarchy (ANCSH) - a canonical representation for different articulated objects in a given category. As the key to achieve intra-category generalization, the representation constructs a canonical object space as well as a set of canonical part spaces. The canonical object space normalizes the object orientation, scales and articulations (e.g. joint parameters and states) while each canonical part space further normalizes its part pose and scale. We develop a deep network based on PointNet++ that predicts ANCSH from a single depth point cloud, including part segmentation, normalized coordinates, and joint parameters in the canonical object space. By leveraging the canonicalized joints, we demonstrate: 1) improved performance in part pose and scale estimations using the induced kinematic constraints from joints; 2) high accuracy for joint parameter estimation in camera space.
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In recent years, deep learning methods have been employed for articulation estimation from raw sensory data.
“In recent years, deep learning methods [1, 22, 31, 32, 39, 40] have been employed for articulation estimation from raw sensory data.”
From Ditto in the House: Building Articulation Models of Indoor Scenes through Interactive Perception (2023) · cites this paper as [22]
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References 34 · Cited by 196
Research works printed but not linked: 26
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- Authors
- Li, X., Wang, H., Yi, L., Guibas, L. J., Abbott, A. L., & Song, S.
- Affiliations
- Virginia Tech (US)
- Stanford University (US)
- Google (United States) (US)
- Columbia University (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 3703-3712
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3035131925
- Cited by
- 196
- References
- 34
- Citations by year
- 2026: 13 · 2025: 35 · 2024: 33 · 2023: 40 · 2022: 40 · 2021: 28 · 2020: 7
- FWCI
- 43.12
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- category-level pose estimation, single depth image, unseen object generalization, joint parameter estimation, part segmentation, kinematic constraints, PointNet
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