Autonomous vehicles require knowledge of the surrounding road layout, which
can be predicted by state-of-the-art CNNs. This work addresses the current lack
of data for determining lane instances, which are needed for various driving
manoeuvres. The main issue is the time-consuming manual labelling process,
typically applied per image. We notice that driving the car is itself a form of
annotation. Therefore, we propose a semi-automated method that allows for
efficient labelling of image sequences by utilising an estimated road plane in
3D based on where the car has driven and projecting labels from this plane into
all images of the sequence. The average labelling time per image is reduced to
5 seconds and only an inexpensive dash-cam is required for data capture. We are
releasing a dataset of 24,000 images and additionally show experimental
semantic segmentation and instance segmentation results.

提出一种半自动化的方法，利用估计的 3D 道路平面投影所有图像序列中的标签，以实现图像序列的高效标注，平均每张图像的标注时间缩短到了 5 秒，无需使用昂贵的传感器设备，同时发布了一个包含 24,000 张图像的数据集并展示了实验结果。