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We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset forautonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial videogame, has a large detailed world with realistic graphics, which provides adiverse data collection environment. Existing works creating synthetic LiDARdata for autonomous driving with GTA V have not released their datasets, relyon an in-game raycasting function which represents people as cylinders, and canfail to capture vehicles past 30 metres. Our work creates a precise LiDARsimulator within GTA V which collides with detailed models for all entities nomatter the type or position. The PreSIL dataset consists of over 50,000 framesand includes high-definition images with full resolution depth information,semantic segmentation (images), point-wise segmentation (point clouds), anddetailed annotations for all vehicles and people. Collecting additional datawith our framework is entirely automatic and requires no human annotation ofany kind. We demonstrate the effectiveness of our dataset by showing animprovement of up to 5% average precision on the KITTI 3D Object Detectionbenchmark challenge when state-of-the-art 3D object detection networks arepre-trained with our data. The data and code are available athttps://tinyurl.com/y3tb9sxy

Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception