Learning Discriminative Features by Covering Local Geometric Space for Point Cloud Analysis
Author:
Affiliation:
1. Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China
2. School of Computer Science and Engineering, Beihang University, Beijing, China
Funder
National Natural Science Foundation of China
Key Research Program of the Chinese Academy of Sciences
Key-Area Research and Development Program of Guangdong Province
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Earth and Planetary Sciences,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/36/9633014/09762976.pdf?arnumber=9762976
Reference73 articles.
1. Learning and memorizing representative prototypes for 3D point cloud semantic and instance segmentation;he;Computer Vision,2020
2. AGNet: An Attention-Based Graph Network for Point Cloud Classification and Segmentation
3. Self-Prediction for Joint Instance and Semantic Segmentation of Point Clouds
4. HAPGN: Hierarchical Attentive Pooling Graph Network for Point Cloud Segmentation
5. CondConv: Conditionally parameterized convolutions for efficient inference;yang;arXiv 1904 04971,2019
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