Accurate Pose Estimation of the Texture-Less Objects With Known CAD Models via Point Cloud Matching
Author:
Affiliation:
1. Guangdong Key Laboratory of Precision Equipment and Manufacturing Technology, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China
Funder
National Natural Science Foundation of China
Guangzhou Municipal Science and Technology Project of China
Young Talent Support Project of Guangzhou Association for Science and Technology
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Instrumentation
Link
http://xplorestaging.ieee.org/ielx7/7361/10302460/10261416.pdf?arnumber=10261416
Reference37 articles.
1. StickyPillars: Robust and Efficient Feature Matching on Point Clouds using Graph Neural Networks
2. Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes
3. PointNet: Deep learning on point sets for 3D classification and segmentation;qi;Proc IEEE Comput Vis Pattern Recognit,2017
4. T-LESS: An RGB-D Dataset for 6D Pose Estimation of Texture-Less Objects
5. Using spin images for efficient object recognition in cluttered 3D scenes
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