论文标题
Synwoodscape：用于自动驾驶的合成环境视图Fisheye摄像机数据集
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving
论文作者
论文摘要
环绕视图相机是用于自动驾驶的主要传感器，用于近场感知。它是主要用于停车可视化和自动停车的商用车中最常用的传感器之一。四个带有190°视场的鱼眼摄像机覆盖了车辆周围的360°。由于其高径向失真，标准算法不容易扩展。以前，我们发布了第一个名为Woodscape的公共鱼眼环境视图数据集。在这项工作中，我们发布了环境视图数据集的合成版本，涵盖了其许多弱点并扩展了它。首先，不可能获得像素光流和深度的地面真相。其次，为了采样不同的框架，木景没有同时注释的所有四个相机。但是，这意味着不能设计多相机算法是为了在新数据集中启用的鸟眼空间中获得统一的输出。我们在Carla模拟器中实现了环绕式Fisheye几何预测，与木观的配置相匹配并创建了Synwoodscape。我们从合成数据集中释放80k图像，并带有10个以上任务的注释。我们还发布了基线代码和支持脚本。
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° field of view cover the 360° 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.
