Semantic Point Cloud Segmentation Based on Hexagonal Klemperer Rosette and Machine Learning-Reference-Cited by-同舟云学术

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Semantic Point Cloud Segmentation Based on Hexagonal Klemperer Rosette and Machine Learning

Published:2023 Issue: Volume: Page:617-629
ISSN:2195-4356
Container-title:Lecture Notes in Mechanical Engineering
language:
Short-container-title:

Author:

Balado Jesús,Fernández Antonio,González Elena,Díaz-Vilariño Lucía

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-20325-1_49

Reference29 articles.

1. Bello SA, Yu S, Wang C et al (2020) Review: deep learning on 3D point clouds. Remote Sens 12

2. Ni H, Lin X, Zhang J (2017) Classification of ALS point cloud with improved point cloud segmentation and random forests. Remote Sens 9

3. Biçici S, Zeybek M (2021) Effectiveness of training sample and features for random forest on road extraction from unmanned aerial vehicle-based point cloud. Transp Res Rec 2675:401–418. https://doi.org/10.1177/03611981211029645

4. Lin W, Fan W, Liu H, et al (2021) Classification of handheld laser scanning tree point cloud based on different KNN algorithms and random forest algorithm. Forests 12

5. Charles RQ, Su H, Kaichun M, Guibas LJ (2017) PointNet: deep learning on point sets for 3D classification and segmentation. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 77–85

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SEMANTIC POINT CLOUD SEGMENTATION IN URBAN ENVIRONMENTS WITH 1D CONVOLUTIONAL NEURAL NETWORKS;The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences;2024-03-08

2. TREE DIGITISATION FROM POINT CLOUDS WITH UNREAL ENGINE;ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences;2023-12-05

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