论文标题
LIDAR语义分割中的域适应性通过对齐类分布
Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions
论文作者
论文摘要
LIDAR语义细分提供了有关环境的3D语义信息，这是智能系统在决策过程中的重要提示。深度神经网络正在以大型公共基准在这项任务上取得最新的结果。不幸的是，找到可以很好地推广或适应数据分布不同的其他域的模型仍然是一个主要挑战。这项工作解决了激光雷达语义分割模型的无监督域适应性问题。我们的方法将新颖的想法结合在当前的最新方法之上，并产生新的最新结果。我们提出了简单但有效的策略，以通过对齐输入空间上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，该方法将目标域语义类别的分布与源域的分布保持一致。提出的消融研究表明了每个部分如何促进最终性能。我们的策略表明，与在三个不同域上进行比较的域适应性胜过以前的方法。
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.
