Improving performance of deep learning models for 3D point cloud semantic segmentation via attention mechanisms-Reference-Cited by-同舟云学术

Improving performance of deep learning models for 3D point cloud semantic segmentation via attention mechanisms

Published:2022-08 Issue: Volume:106 Page:277-287
ISSN:0097-8493
Container-title:Computers & Graphics
language:en
Short-container-title:Computers & Graphics

Author:

Vanian Vazgen,Zamanakos Georgios^ORCID,Pratikakis Ioannis

Publisher

Elsevier BV

Subject

Computer Graphics and Computer-Aided Design,Human-Computer Interaction,General Engineering

Reference44 articles.

1. Qi CR, Su H, Mo K, Guibas LJ. Pointnet: Deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, p. 652–660.

2. Pointnet++: Deep hierarchical feature learning on point sets in a metric space;Qi,2017

3. Rangenet++: Fast and accurate lidar semantic segmentation;Milioto,2019

4. Voxnet: A 3D convolutional neural network for real-time object recognition;Maturana,2015

5. 4D spatio-temporal convnets: Minkowski convolutional neural networks;Choy,2019

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