Our the open-access data set, ApolloScape, is a part of the Apollo project, the only open source autonomous driving platform which was initiated by Baidu Inc in 2017. In order to capture the static 3D world with high granularity, we used a mobile LIDAR scanner from Riegl to collect point clouds, which yields point cloud densities much higher than those from Velodyne (which was used by KITTI). In addition, two high-resolution cameras at the head of the collection car were synchronized and calibrated, recording at a frame rate of 30 fps. Each camera has high precision GPS and IMU, so that the accurate camera pose is recorded on-the-fly. All our videos were recorded from cities, e.g. Beijing, Shanghai and Shenzhen, in China.
In Tab. 1, we show the properties of our dataset compared to existing ones. In particular, we have high-quality 3D labels from realistic scenes for both the static background and moving objects. Currently, we already have 50K images labeled covering around 10 km from three sites in three cities. Moreover, each area was scanned repeatedly under various weather and lighting conditions. Finally, ApolloScape will be an evolving dataset and labeled data from new cities will be added monthly. We plan to have at least 200K images, consisting of 20 km road covering 5 sites from three cities, for holding the challenge. In the following, we will introduce the details of each challenge specifically targeting autonomous driving.