Surround-view cameras are a primary sensor for automated driving, used for near-field perception. It is one of the most commonly used sensors in commercial vehicles primarily used for parking visualization and automated parking. Four fisheye cameras with a 190{\deg} field of view cover the 360{\deg} around the vehicle. Due to its high radial distortion, the standard algorithms do not extend easily. Previously, we released the first public fisheye surround-view dataset named WoodScape. In this work, we release a synthetic version of the surround-view dataset, covering many of its weaknesses and extending it. Firstly, it is not possible to obtain ground truth for pixel-wise optical flow and depth. Secondly, WoodScape did not have all four cameras annotated simultaneously in order to sample diverse frames. However, this means that multi-camera algorithms cannot be designed to obtain a unified output in birds-eye space, which is enabled in the new dataset. We implemented surround-view fisheye geometric projections in CARLA Simulator matching WoodScape's configuration and created SynWoodScape. We release 80k images from the synthetic dataset with annotations for 10+ tasks. We also release the baseline code and supporting scripts.

翻译：闭路摄影机是用于近距离感知的自动驾驶的主要传感器,是商用车辆中最常用的传感器之一,主要用于停车视觉化和自动停车。四台有190~deg}视野的鱼眼照相机覆盖了车辆周围的360×deg}。标准算法并不易扩展。以前,我们发布了第一个名为WoodScape的公共鱼眼环景数据集。在这项工作中,我们发布了一个环形数据集的合成版本,覆盖了许多弱点并扩展了该数据集。首先,不可能获得像素光学光学流和深度的地面真相。第二,WoodScape没有同时加注所有四台照相机,以取样不同的框架。然而,这意味着多镜头算法无法设计成在鸟眼空间获得统一输出,而新数据集中启用了这种输出。我们在CARLA Simula Simator中进行了环形鱼眼地球测量预测,覆盖了它的许多弱点,并创建了SynWoodScapecle的光学流和深度。第二,WoodScapeat没有同时加注解所有四个摄像头,用以取样用于10号的合成数据发布。我们还发布了80和脚本。