This work aims to address the challenges in domain adaptation of 3D object detection using infrastructure LiDARs. We design a model DASE-ProPillars that can detect vehicles in infrastructure-based LiDARs in real-time. Our model uses PointPillars as the baseline model with additional modules to improve the 3D detection performance. To prove the effectiveness of our proposed modules in DASE-ProPillars, we train and evaluate the model on two datasets, the open source A9-Dataset and a semi-synthetic infrastructure dataset created within the Regensburg Next project. We do several sets of experiments for each module in the DASE-ProPillars detector that show that our model outperforms the SE-ProPillars baseline on the real A9 test set and a semi-synthetic A9 test set, while maintaining an inference speed of 45 Hz (22 ms). We apply domain adaptation from the semi-synthetic A9-Dataset to the semi-synthetic dataset from the Regensburg Next project by applying transfer learning and achieve a 3D [email protected] of 93.49% on the Car class of the target test set using 40 recall positions.

翻译：本研究旨在解决基础设施激光雷达三维目标检测中的域适应挑战。我们设计了DASE-ProPillars模型，该模型能够实时检测基于基础设施的激光雷达中的车辆。模型以PointPillars为基线，并增加额外模块以提升三维检测性能。为验证DASE-ProPillars中提出模块的有效性，我们在两个数据集上训练和评估模型：开源A9数据集以及雷根斯堡Next项目创建的半合成基础设施数据集。我们对DASE-ProPillars检测器中的每个模块进行了多组实验，结果表明，我们的模型在真实A9测试集和半合成A9测试集上均优于SE-ProPillars基线，同时保持45 Hz（22毫秒）的推理速度。通过迁移学习，我们将域适应从半合成A9数据集扩展到雷根斯堡Next项目的半合成数据集，在目标测试集的Car类别上，使用40个召回位置实现了93.49%的三维平均精度（[email protected]）。