Point cloud data from 3D LiDAR sensors are one of the most crucial sensor modalities for versatile safety-critical applications such as self-driving vehicles. Since the annotations of point cloud data is an expensive and time-consuming process, therefore recently the utilisation of simulated environments and 3D LiDAR sensors for this task started to get some popularity. With simulated sensors and environments, the process for obtaining an annotated synthetic point cloud data became much easier. However, the generated synthetic point cloud data are still missing the artefacts usually exist in point cloud data from real 3D LiDAR sensors. As a result, the performance of the trained models on this data for perception tasks when tested on real point cloud data is degraded due to the domain shift between simulated and real environments. Thus, in this work, we are proposing a domain adaptation framework for bridging this gap between synthetic and real point cloud data. Our proposed framework is based on the deep cycle-consistent generative adversarial networks (CycleGAN) architecture. We have evaluated the performance of our proposed framework on the task of vehicle detection from a bird's eye view (BEV) point cloud images coming from real 3D LiDAR sensors. The framework has shown competitive results with an improvement of more than 7% in average precision score over other baseline approaches when tested on real BEV point cloud images.
翻译:3D LiDAR 传感器 3D LiDAR 传感器的点云数据是多功能安全关键应用(如自驾驶飞行器)的最关键传感器模式之一。 由于点云数据说明是一个昂贵和耗时的过程,因此最近对模拟环境和3D LiDAR 传感器的利用开始受到某种欢迎。由于模拟传感器和环境,获取一个附加说明的合成点云数据的过程变得容易得多。然而,生成的合成点云数据仍然缺少实际的3D LiDAR 传感器的点云数据中通常存在的人工制品。因此,在实际点云数据测试时,关于这种观测任务数据的培训模型的性能由于模拟点与实际环境之间的域变化而退化。因此,我们在此工作中提出了缩小合成点云数据与实际点云数据之间这一差距的域适应框架。我们提议的框架以深周期一致的变色对抗网络(CycleGAN)结构为基础。我们从鸟眼视图观测车辆的拟议框架(BEVEV) 7级模型的性能表现由于模拟点与实际3D 级图像的升级框架相比,在实际3D 水平上展示了比其他图像的平均结果。