LiDAR semantic segmentation provides 3D semantic information about the environment, an essential cue for intelligent systems during their decision making processes. Deep neural networks are achieving state-of-the-art results on large public benchmarks on this task. Unfortunately, finding models that generalize well or adapt to additional domains, where data distribution is different, remains a major challenge. This work addresses the problem of unsupervised domain adaptation for LiDAR semantic segmentation models. Our approach combines novel ideas on top of the current state-of-the-art approaches and yields new state-of-the-art results. We propose simple but effective strategies to reduce the domain shift by aligning the data distribution on the input space. Besides, we propose a learning-based approach that aligns the distribution of the semantic classes of the target domain to the source domain. The presented ablation study shows how each part contributes to the final performance. Our strategy is shown to outperform previous approaches for domain adaptation with comparisons run on three different domains.

翻译：LiDAR 语义分割法提供了环境的3D语义信息,这是智能系统在决策过程中的基本提示。深神经网络正在就这项任务的大型公共基准取得最先进的结果。 不幸的是,找到将数据分布不同的模型或适应于其他域的模型,这仍然是一个重大挑战。这项工作解决了LiDAR 语义分隔法模型不受监督的域适应问题。我们的方法在目前最先进的方法之上结合了新的概念,并产生了新的最新结果。我们提出了简单而有效的战略,通过协调输入空间的数据分布来减少域的转移。此外,我们提出了一种基于学习的方法,将目标域的语义分类分布与源域的分布相匹配。介绍的对比研究显示了每个部分对最后性能的贡献。我们的战略比以往的域适应方法要优于三个不同领域的比较。