Precise Synthetic Image and LiDAR (PreSIL) dataset for autonomous vehicle perception

Released in: 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 work creating synthetic data for autonomous driving with GTA V have not released their datasets and rely on an in-game raycasting function which represents people as cylinders and can fail to capture vehicles past 30 metres.  This 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 instances and includes high-definition images with full resolution depth information, semantic segmentation (images), point-wise segmentation (point clouds), ground point labels (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. The authors demonstrate the effectiveness of their 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.


Images in dataset


Year Released

Key Links & Stats

@article{DBLP:journals/corr/abs-1905-00160, author = {Braden Hurl and Krzysztof Czarnecki and Steven L. Waslander}, title = {Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception}, journal = {CoRR}, volume = {abs/1905.00160}, year = {2019}, url = {}, eprinttype = {arXiv}, eprint = {1905.00160}, timestamp = {Mon, 27 May 2019 13:15:00 +0200}, biburl = {}, bibsource = {dblp computer science bibliography,} }



  1. Still Image
  2. RGB-D
  3. Point Cloud
  4. LiDAR


  1. A/V

ML Task

  1. Object Detection
  2. Semantic Segmentation
  3. Instance Segmentation
  4. Autonomous Vehicles
  5. Depth Estimation
  6. Object Recognition
  7. Object Tracking

Related organizations

University of Waterloo