# Synthetic Dataset Generation for Autonomous Mobile Robots Using 3D Gaussian Splatting for Vision Training

arXiv: https://arxiv.org/abs/2506.05092 · HTML: https://mlsanity.com/paper/2506.05092/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 13 of 13)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items](https://mlsanity.com/paper/2204.11918/) (2022)
- [BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis](https://mlsanity.com/paper/2302.14859/) (2023)
- [Deep Learning vs. Traditional Computer Vision](https://mlsanity.com/paper/1910.13796/) (2019)
- [Synthetic Datasets for Autonomous Driving: A Survey](https://mlsanity.com/paper/2304.12205/) (2023)
- [How much real data do we actually need: Analyzing object detection performance using synthetic and real data](https://mlsanity.com/paper/1907.07061/) (2019)
- [A Review of Object Detection Models based on Convolutional Neural Network](https://mlsanity.com/paper/1905.01614/) (2019)
- [On Rendering Synthetic Images for Training an Object Detector](https://mlsanity.com/paper/1411.7911/) (2014)
- [Realistic Surgical Image Dataset Generation Based On 3D Gaussian Splatting](https://mlsanity.com/paper/2407.14846/) (2024)
- [Self-Supervised Data Generation for Precision Agriculture: Blending Simulated Environments with Real Imagery](https://mlsanity.com/paper/2502.18320/) (2025)

## Built on by (most cited first, 1 of 1)

- [Meshless Domain Randomization via Explicit Parameter Perturbation of 3D Gaussian Splatting](https://mlsanity.com/paper/2607.22890/) (2026)

## Abstract (arXiv)

Annotated datasets are critical for training neural networks for object detection, yet their manual creation is time- and labour-intensive, subjective to human error, and often limited in diversity. This challenge is particularly pronounced in the domain of robotics, where diverse and dynamic scenarios further complicate the creation of representative datasets. To address this, we propose a novel method for automatically generating annotated synthetic data in Unreal Engine. Our approach leverages photorealistic 3D Gaussian splats for rapid synthetic data generation. We demonstrate that synthetic datasets can achieve performance comparable to that of real-world datasets while significantly reducing the time required to generate and annotate data. Additionally, combining real-world and synthetic data significantly increases object detection performance by leveraging the quality of real-world images with the easier scalability of synthetic data. To our knowledge, this is the first application of synthetic data for training object detection algorithms in the highly dynamic and varied environment of robot soccer. Validation experiments reveal that a detector trained on synthetic images performs on par with one trained on manually annotated real-world images when tested on robot soccer match scenarios. Our method offers a scalable and comprehensive alternative to traditional dataset creation, eliminating the labour-intensive error-prone manual annotation process. By generating datasets in a simulator where all elements are intrinsically known, we ensure accurate annotations while significantly reducing manual effort, which makes it particularly valuable for robotics applications requiring diverse and scalable training data.