# Domain Randomization for Scene-Specific Car Detection and Pose Estimation

arXiv: https://arxiv.org/abs/1811.05939 · HTML: https://mlsanity.com/paper/1811.05939/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 23 of 23)

- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [NICO++: Towards Better Benchmarking for Domain Generalization](https://mlsanity.com/paper/2204.08040/) (2022)
- [Domain Randomization-Enhanced Depth Simulation and Restoration for Perceiving and Grasping Specular and Transparent Objects](https://mlsanity.com/paper/2208.03792/) (2022)
- [Task2Sim : Towards Effective Pre-training and Transfer from Synthetic Data](https://mlsanity.com/paper/2112.00054/) (2021)
- [Intra-Source Style Augmentation for Improved Domain Generalization](https://mlsanity.com/paper/2210.10175/) (2022)
- [The Overcooked Generalisation Challenge: Evaluating Cooperation with Novel Partners in Unknown Environments Using Unsupervised Environment Design](https://mlsanity.com/paper/2406.17949/) (2024)
- [Intra- & Extra-Source Exemplar-Based Style Synthesis for Improved Domain Generalization](https://mlsanity.com/paper/2307.00648/) (2023)
- [Domain Generalisation for Object Detection under Covariate and Concept Shift](https://mlsanity.com/paper/2203.05294/) (2022)
- [Persistent Homology Meets Object Unity: Object Recognition in Clutter](https://mlsanity.com/paper/2305.03815/) (2023)
- [INSURE: An Information Theory Inspired Disentanglement and Purification Model for Domain Generalization](https://mlsanity.com/paper/2309.04063/) (2023)
- [Conditional entropy minimization principle for learning domain invariant representation features](https://mlsanity.com/paper/2201.10460/) (2022)
- [HaDR: Applying Domain Randomization for Generating Synthetic Multimodal Dataset for Hand Instance Segmentation in Cluttered Industrial Environments](https://mlsanity.com/paper/2304.05826/) (2023)
- [Sequential Ensembling for Semantic Segmentation](https://mlsanity.com/paper/2210.05387/) (2022)
- [Leveraging Systematic Knowledge of 2D Transformations](https://mlsanity.com/paper/2206.00893/) (2022)
- [HazardNet: Road Debris Detection by Augmentation of Synthetic Models](https://mlsanity.com/paper/2303.07547/) (2023)
- [Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models](https://mlsanity.com/paper/2305.14621/) (2023)
- [Diagnosing and Rectifying Fake OOD Invariance: A Restructured Causal Approach](https://mlsanity.com/paper/2312.09758/) (2023)
- [UAV-CROWD: Violent and non-violent crowd activity simulator from the perspective of UAV](https://mlsanity.com/paper/2208.06702/) (2022)
- [Towards Radar Emitter Recognition in Changing Environments with Domain Generalization](https://mlsanity.com/paper/2302.09359/) (2023)
- [Prompting-based Temporal Domain Generalization](https://mlsanity.com/paper/2310.02473/) (2023)
- [Enhancing Deep Learning based RMT Data Inversion using Gaussian Random Field](https://mlsanity.com/paper/2410.19858/) (2024)
- [ArcGen: Generalizing Neural Backdoor Detection Across Diverse Architectures](https://mlsanity.com/paper/2512.19730/) (2025)

## Abstract (arXiv)

We address the issue of domain gap when making use of synthetic data to train a scene-specific object detector and pose estimator. While previous works have shown that the constraints of learning a scene-specific model can be leveraged to create geometrically and photometrically consistent synthetic data, care must be taken to design synthetic content which is as close as possible to the real-world data distribution. In this work, we propose to solve domain gap through the use of appearance randomization to generate a wide range of synthetic objects to span the space of realistic images for training. An ablation study of our results is presented to delineate the individual contribution of different components in the randomization process. We evaluate our method on VIRAT, UA-DETRAC, EPFL-Car datasets, where we demonstrate that using scene specific domain randomized synthetic data is better than fine-tuning off-the-shelf models on limited real data.