Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception
This provides a scalable, automatically generated dataset for researchers and developers in autonomous driving, addressing data collection bottlenecks, though it is incremental over existing synthetic methods.
The authors tackled the lack of high-quality synthetic LiDAR datasets for autonomous vehicle perception by creating the PreSIL dataset using a precise simulator in GTA V, resulting in up to a 5% improvement in average precision on the KITTI 3D Object Detection benchmark.
We introduce the Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception. Grand Theft Auto V (GTA V), a commercial video game, has a large detailed world with realistic graphics, which provides a diverse data collection environment. Existing works creating synthetic LiDAR data for autonomous driving with GTA V have not released their datasets, rely on an in-game raycasting function which represents people as cylinders, and can fail to capture vehicles past 30 metres. Our work creates a precise LiDAR simulator within GTA V which collides with detailed models for all entities no matter the type or position. The PreSIL dataset consists of over 50,000 frames and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), and detailed annotations for all vehicles and people. Collecting additional data with our framework is entirely automatic and requires no human annotation of any kind. We demonstrate the effectiveness of our dataset by showing an improvement of up to 5% average precision on the KITTI 3D Object Detection benchmark challenge when state-of-the-art 3D object detection networks are pre-trained with our data. The data and code are available at https://tinyurl.com/y3tb9sxy