The costly and time-consuming annotation process to produce large training sets for modelling semantic LiDAR segmentation methods has motivated the development of semi-supervised learning (SSL) methods. However, such SSL approaches often concentrate on employing consistency learning only for individual LiDAR representations. This narrow focus results in limited perturbations that generally fail to enable effective consistency learning. Additionally, these SSL approaches employ contrastive learning based on the sampling from a limited set of positive and negative embedding samples. This paper introduces a novel semi-supervised LiDAR semantic segmentation framework called ItTakesTwo (IT2). IT2 is designed to ensure consistent predictions from peer LiDAR representations, thereby improving the perturbation effectiveness in consistency learning. Furthermore, our contrastive learning employs informative samples drawn from a distribution of positive and negative embeddings learned from the entire training set. Results on public benchmarks show that our approach achieves remarkable improvements over the previous state-of-the-art (SOTA) methods in the field. The code is available at: https://github.com/yyliu01/IT2.

该研究提出了一种名为ItTakesTwo（IT2）的半监督LiDAR语义分割框架，通过确保对等LiDAR表示的一致预测，提高了一致性学习中的扰动效果，并且利用从整个训练集中学习到的正负嵌入样本分布进行对比学习，公开数据集的结果显示，我们的方法在该领域取得了显著改进。

ItTakesTwo：借助点云的对等表示进行半监督 LiDAR 语义分割