Scene parsing aims to assign a class (semantic) label for each pixel in an image, or each point in a
point cloud. It is one of the most comprehensive analyses of a 2D/3D scene. Given the rise of
autonomous driving, environmental perception is expected to be a key enabling technical piece.
The ApolloScape dataset provided by Baidu, Inc. will include RGB videos with high resolution images
and per pixel annotation, survey- grade dense 3D points with semantic segmentation, stereoscopic
video, and panoramic images.
We equipped a mid-size SUV with high resolution cameras and a Riegl acquisition system. Our dataset is collected in different cities under various traffic conditions. The number of moving objects, such as vehicles and pedestrians, averages from tens to over one hundred. Moreover, each image is tagged with high-accuracy pose information at cm accuracy and the static background point cloud has mm relative accuracy. We expect our new dataset can deeply benefit various autonomous driving related applications that include but not limited to 2D/3D scene understanding, localization, transfer learning, and driving simulation.