Detecting vehicles and representing their position and orientation in the
three dimensional space is a key technology for autonomous driving. Recently,
methods for 3D vehicle detection solely based on monocular RGB images gained
popularity. In order to facilitate this task as well as to compare and drive
state-of-the-art methods, several new datasets and benchmarks have been
published. Ground truth annotations of vehicles are usually obtained using
lidar point clouds, which often induces errors due to imperfect calibration or
synchronization between both sensors. To this end, we propose Cityscapes 3D,
extending the original Cityscapes dataset with 3D bounding box annotations for
all types of vehicles. In contrast to existing datasets, our 3D annotations
were labeled using stereo RGB images only and capture all nine degrees of
freedom. This leads to a pixel-accurate reprojection in the RGB image and a
higher range of annotations compared to lidar-based approaches. In order to
ease multitask learning, we provide a pairing of 2D instance segments with 3D
bounding boxes. In addition, we complement the Cityscapes benchmark suite with
3D vehicle detection based on the new annotations as well as metrics presented
in this work. Dataset and benchmark are available online.

该研究提出一种基于单目 RGB 图像的三维车辆检测方法，为此创建了 Cityscapes 3D 数据集，包含了所有类型车辆的三维标注信息，使得与基于 lidar 的方法相比具有更高效率和更大范围的标注信息。此外，研究将 2D 实例分割与 3D 包围盒成对提供，以便多任务学习。这对自动驾驶技术有着很重要的意义。