# On Rendering Synthetic Images for Training an Object Detector

arXiv: https://arxiv.org/abs/1411.7911 · HTML: https://mlsanity.com/paper/1411.7911/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2014.

## Built on by (most cited first, 18 of 18)

- [Fake It Till You Make It: Face analysis in the wild using synthetic data alone](https://mlsanity.com/paper/2109.15102/) (2021)
- [StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners](https://mlsanity.com/paper/2306.00984/) (2023)
- [Scaling Laws of Synthetic Images for Model Training ... for Now](https://mlsanity.com/paper/2312.04567/) (2023)
- [Is Synthetic Data all We Need? Benchmarking the Robustness of Models Trained with Synthetic Images](https://mlsanity.com/paper/2405.20469/) (2024)
- [A Vision Check-up for Language Models](https://mlsanity.com/paper/2401.01862/) (2024)
- [Evaluating a Synthetic Image Dataset Generated with Stable Diffusion](https://mlsanity.com/paper/2211.01777/) (2022)
- [Mesh-Tension Driven Expression-Based Wrinkles for Synthetic Faces](https://mlsanity.com/paper/2210.03529/) (2022)
- [Learning from Synthetic Data for Visual Grounding](https://mlsanity.com/paper/2403.13804/) (2024)
- [DeePaste -- Inpainting for Pasting](https://mlsanity.com/paper/2112.10600/) (2021)
- [Image change detection with only a few samples](https://mlsanity.com/paper/2311.03762/) (2023)
- [Drone Detection using Deep Neural Networks Trained on Pure Synthetic Data](https://mlsanity.com/paper/2411.09077/) (2024)
- [Synthetic Dataset Generation for Autonomous Mobile Robots Using 3D Gaussian Splatting for Vision Training](https://mlsanity.com/paper/2506.05092/) (2025)
- [Beyond the Seen: Bounded Distribution Estimation for Open-Vocabulary Learning](https://mlsanity.com/paper/2510.04770/) (2025)
- [Instance-Level Generation for Representation Learning](https://mlsanity.com/paper/2510.09171/) (2025)
- [Utilizing synthetic training data for the supervised classification of rat ultrasonic vocalizations](https://mlsanity.com/paper/2303.03183/) (2023)
- [Everything to the Synthetic: Diffusion-driven Test-time Adaptation via Synthetic-Domain Alignment](https://mlsanity.com/paper/2406.04295/) (2024)
- [DiffRenderGAN: Addressing Training Data Scarcity in Deep Segmentation Networks for Quantitative Nanomaterial Analysis through Differentiable Rendering and Generative Modelling](https://mlsanity.com/paper/2502.09477/) (2025)
- [Systematic Evaluation of Novel View Synthesis for Video Place Recognition](https://mlsanity.com/paper/2603.05876/) (2026)

## Abstract (arXiv)

We propose a novel approach to synthesizing images that are effective for training object detectors. Starting from a small set of real images, our algorithm estimates the rendering parameters required to synthesize similar images given a coarse 3D model of the target object. These parameters can then be reused to generate an unlimited number of training images of the object of interest in arbitrary 3D poses, which can then be used to increase classification performances. A key insight of our approach is that the synthetically generated images should be similar to real images, not in terms of image quality, but rather in terms of features used during the detector training. We show in the context of drone, plane, and car detection that using such synthetically generated images yields significantly better performances than simply perturbing real images or even synthesizing images in such way that they look very realistic, as is often done when only limited amounts of training data are available.