HAVANA: Hard Negative Sample-Aware Self-Supervised Contrastive Learning for Airborne Laser Scanning Point Cloud Semantic Segmentation

HAVANA: Hard Negative Sample-Aware Self-Supervised Contrastive Learning for Airborne Laser Scanning Point Cloud Semantic Segmentation

Deep Neural Network (DNN)-based point cloud semantic segmentation has presented significant breakthrough using large-scale labeled aerial laser point cloud datasets. However, annotating such large-scaled point clouds is time-consuming. Self-Supervised Learning (SSL) is a promising approach to this p...

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Bibliographic Details
Main Authors: Yunsheng Zhang, Jianguo Yao, Ruixiang Zhang, Xuying Wang, Siyang Chen, Han Fu
Format: Article
Language:English
Published: MDPI AG 2024-01-01
Series:Remote Sensing
Subjects:
ALS point cloud
semantic segmentation
self-supervision
end-to-end
Online Access:https://www.mdpi.com/2072-4292/16/3/485

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https://www.mdpi.com/2072-4292/16/3/485

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