Realistic vehicle sensor simulation is an important element in developing
autonomous driving. As physics-based implementations of visual sensors like
LiDAR are complex in practice, data-based approaches promise solutions. Using
pairs of camera images and LiDAR scans from real test drives, GANs can be
trained to translate between them. For this process, we contribute two
additions. First, we exploit the camera images, acquiring segmentation data and
dense depth maps as additional input for training. Second, we test the
performance of the LiDAR simulation by testing how well an object detection
network generalizes between real and synthetic point clouds to enable
evaluation without ground truth point clouds. Combining both, we simulate LiDAR
point clouds and demonstrate their realism.

开发自动驾驶中，真实车辆传感器模拟是重要的因素。我们提出利用基于数据的方法，使用来自实际测试驾驶的相机图像和 LiDAR 扫描数据训练生成对抗网络，实现相互翻译。通过添加分割数据和密集深度图像的相机图像，以及通过测试真实与合成点云之间物体检测网络的泛化效果评估 LiDAR 模拟性能，我们结合这两种方法，模拟并展示了真实的 LiDAR 点云。