PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D Pose Estimation
Author:
Affiliation:
1. Beihang University,State Key Laboratory of Software Development Environment,Beijing,China
2. Beihang University,School of Computer Science and Engineering,Beijing,China
Funder
National Natural Science Foundation of China
State Key Laboratory of Software Development Environment
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9709627/9709628/09711034.pdf?arnumber=9711034
Reference47 articles.
1. Dynamic graph cnn for learning on point clouds;wang,2018
2. DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
3. Faster R-CNN: towards real-time object detection with region proposal networks;ren;Conference on Neural Information Processing Systems,2015
4. BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth
5. Frustum PointNets for 3D Object Detection from RGB-D Data
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