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

## TL;DR

PreSIL is a comprehensive synthetic dataset generated from GTA V, providing detailed images and LiDAR data for autonomous vehicle perception, improving 3D object detection accuracy.

## Contribution

We developed a precise LiDAR simulation within GTA V and released a large, annotated synthetic dataset with automatic data collection, surpassing previous methods.

## Key findings

- Up to 5% improvement in 3D detection accuracy on KITTI benchmark
- Generated over 50,000 frames with detailed annotations
- Automatic data collection without human annotation

## Abstract

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