DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field
Author:
Affiliation:
1. Beijing University of Posts and Telecommunications, Beijing, China
2. Midea Group, Beijing, China
3. Midea Group, Shanghai, China
Funder
National Key R&D Program of China
BUPT Excellent Ph.D. Students Foundation
China Postdoctoral Science Foundation
National Natural Science Foundation
Science and Technology Innovation Action Plan of Shanghai
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3581783.3612142
Reference48 articles.
1. A polyhedron representation for computer vision
2. Learning Canonical Shape Space for Category-Level 6D Object Pose and Size Estimation
3. SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation
4. FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism
5. Learning Implicit Fields for Generative Shape Modeling
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1. Object Recognition from 3D Point Clouds : A Survey for Beginners;Journal of the Japan Society for Precision Engineering;2024-08-05
2. SM3: Self-supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13
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