Scene understanding is a pivotal task for autonomous vehicles to safely navigate in the environment. Recent advances in deep learning enable accurate semantic reconstruction of the surroundings from LiDAR data. However, these models encounter a large domain gap while deploying them on vehicles equipped with different LiDAR setups which drastically decreases their performance. Fine-tuning the model for every new setup is infeasible due to the expensive and cumbersome process of recording and manually labeling new data. Unsupervised Domain Adaptation (UDA) techniques are thus essential to fill this domain gap and retain the performance of models on new sensor setups without the need for additional data labeling. In this paper, we propose AdaptLPS, a novel UDA approach for LiDAR panoptic segmentation that leverages task-specific knowledge and accounts for variation in the number of scan lines, mounting position, intensity distribution, and environmental conditions. We tackle the UDA task by employing two complementary domain adaptation strategies, data-based and model-based. While data-based adaptations reduce the domain gap by processing the raw LiDAR scans to resemble the scans in the target domain, model-based techniques guide the network in extracting features that are representative for both domains. Extensive evaluations on three pairs of real-world autonomous driving datasets demonstrate that AdaptLPS outperforms existing UDA approaches by up to 6.41 pp in terms of the PQ score.
翻译:环境安全导航是自主工具的关键任务之一。最近深层次学习的进步使得能够根据LIDAR数据对周围环境进行准确的语义重建。 但是,这些模型在安装有不同LIDAR设置的车辆时遇到了巨大的领域差距,而将这些模型安装在配备了不同LIDAR配置的车辆上,从而大大降低了它们的性能。由于记录和人工标记新数据的过程既昂贵又繁琐,因此无法对每个新设置的模型进行微调。因此,不受监督的DODA适应(UDA)技术对于填补这一域差距和保留新传感器设置模型的性能至关重要。在本文件中,我们建议SDDLPS(SDLP),这是一个新的UDA(UDA)光学分类方法,它利用特定任务的知识,说明扫描线数量的变化、定位、强度分布和环境条件。我们通过使用两个互补的域域适应战略,即基于数据和模型的适应技术,通过处理原始LAR扫描来缩小域域间差距,而不需要额外的数据标签。在SDBAS(SDRAS)数据库中,模型化的域图中展示了现有数据库的系列模型,用以展示现有数据库的域域图样样样样样。