GNN-Based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection-Reference-Cited by-同舟云学术

GNN-Based Point Cloud Maps Feature Extraction and Residual Feature Fusion for 3D Object Detection

Published:2023-05-29 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Liao Wei-Hsiang¹,Wang Chieh-Chih²,Lin Wen-Chieh³

Affiliation:

1. National Yang Ming Chiao Tung University,Graduate Degree Program of Artificial Intelligence,Hsinchu,Taiwan

2. National Yang Ming Chiao Tung University,Department of Electrical and Computer Engineering,Hsinchu,Taiwan

3. Institute of Multimedia Engineering, National Yang Ming Chiao Tung University,Hsinchu,Taiwan

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10160211/10160212/10160932.pdf?arnumber=10160932

Reference21 articles.

1. TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers

2. End-to-end object detection with transformers;carion;European Conference on Computer Vision,2020

3. Hdnet: Exploiting hd maps for 3d object detection;yang;Proceedings of The 2nd Conference on Robot Learning ser Proceedings of Machine Learning Research,2018

4. SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud

5. Clustering to minimize the maximum intercluster distance

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