Airborne LiDAR systems have the capability to capture the Earth's surface by generating extensive point cloud data comprised of points mainly defined by 3D coordinates. However, labeling such points for supervised learning tasks is time-consuming. As a result, there is a need to investigate techniques that can learn from unlabeled data to significantly reduce the number of annotated samples. In this work, we propose to train a self-supervised encoder with Barlow Twins and use it as a pre-trained network in the task of semantic scene segmentation. The experimental results demonstrate that our unsupervised pre-training boosts performance once fine-tuned on the supervised task, especially for under-represented categories.

翻译：机载LiDAR系统能够通过生成主要由三维坐标定义的点云数据来捕获地球表面。然而，为监督学习任务标注这类点云十分耗时。因此，有必要探究能从无标注数据中学习的技术，以显著减少所需标注样本的数量。本研究提出采用Barlow Twins方法训练自监督编码器，并将其作为语义场景分割任务的预训练网络。实验结果表明，在监督任务上进行微调后，我们的无监督预训练能提升性能，尤其对少数类别效果显著。