# From Gaming to Research: GTA V for Synthetic Data Generation for Robotics and Navigations

arXiv: https://arxiv.org/abs/2502.12303 · HTML: https://mlsanity.com/paper/2502.12303/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 17 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Playing for Data: Ground Truth from Computer Games](https://mlsanity.com/paper/1608.02192/) (2016)
- [Virtual KITTI 2](https://mlsanity.com/paper/2001.10773/) (2020)
- [Long-term Human Motion Prediction with Scene Context](https://mlsanity.com/paper/2007.03672/) (2020)
- [RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation](https://mlsanity.com/paper/2403.06341/) (2024)
- [The Newer College Dataset: Handheld LiDAR, Inertial and Vision with Ground Truth](https://mlsanity.com/paper/2003.05691/) (2020)
- [Are We Ready for Service Robots? The OpenLORIS-Scene Datasets for Lifelong SLAM](https://mlsanity.com/paper/1911.05603/) (2019)
- [Single-View Place Recognition under Seasonal Changes](https://mlsanity.com/paper/1808.06516/) (2018)
- [Precise Synthetic Image and LiDAR (PreSIL) Dataset for Autonomous Vehicle Perception](https://mlsanity.com/paper/1905.00160/) (2019)
- [The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection](https://mlsanity.com/paper/2204.12831/) (2022)
- [Leveraging Synthetic Data in Object Detection on Unmanned Aerial Vehicles](https://mlsanity.com/paper/2112.12252/) (2021)
- [High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation](https://mlsanity.com/paper/2305.01732/) (2023)
- [G2D: from GTA to Data](https://mlsanity.com/paper/1806.07381/) (2018)
- [Benchmarking and Comparing Popular Visual SLAM Algorithms](https://mlsanity.com/paper/1811.09895/) (2018)

## Built on by (most cited first, 2 of 2)

- [REGEN: Real-Time Photorealism Enhancement in Games via a Dual-Stage Generative Network Framework](https://mlsanity.com/paper/2508.17061/) (2025)
- [Training-Free Metrics for Synthetic Object Detection Data: A Proxy for Detector Performance](https://mlsanity.com/paper/2606.19817/) (2026)

## Abstract (arXiv)

In computer vision, the development of robust algorithms capable of generalizing effectively in real-world scenarios more and more often requires large-scale datasets collected under diverse environmental conditions. However, acquiring such datasets is time-consuming, costly, and sometimes unfeasible. To address these limitations, the use of synthetic data has gained attention as a viable alternative, allowing researchers to generate vast amounts of data while simulating various environmental contexts in a controlled setting. In this study, we investigate the use of synthetic data in robotics and navigation, specifically focusing on Simultaneous Localization and Mapping (SLAM) and Visual Place Recognition (VPR). In particular, we introduce a synthetic dataset created using the virtual environment of the video game Grand Theft Auto V (GTA V), along with an algorithm designed to generate a VPR dataset, without human supervision. Through a series of experiments centered on SLAM and VPR, we demonstrate that synthetic data derived from GTA V are qualitatively comparable to real-world data. Furthermore, these synthetic data can complement or even substitute real-world data in these applications. This study sets the stage for the creation of large-scale synthetic datasets, offering a cost-effective and scalable solution for future research and development.