GLSNet++: Global and Local-Stream Feature Fusion for LiDAR Point Cloud Semantic Segmentation Using GNN Demixing Block
Author:
Affiliation:
1. Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA
2. Naval Research Laboratory, Washington, DC, USA
Funder
U.S. Army Research Laboratory
Engineering Research and Development Center–Information Technology Laboratory
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Link
http://xplorestaging.ieee.org/ielx7/7361/10487994/10430108.pdf?arnumber=10430108
Reference58 articles.
1. Deep Learning for 3D Point Clouds: A Survey
2. Generating 3D City Models from Open LiDAR Point Clouds: Advancing Towards Smart City Applications
3. Parallax-Tolerant Aerial Image Georegistration and Efficient Camera Pose Refinement—Without Piecewise Homographies
4. Robust Camera Pose Refinement and Rapid SfM for Multiview Aerial Imagery—Without RANSAC
5. Deep Learning for Image and Point Cloud Fusion in Autonomous Driving: A Review
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