We propose a method that augments a simulated dataset using diffusion models to improve the performance of pedestrian detection in real-world data. The high cost of collecting and annotating data in the real-world has motivated the use of simulation platforms to create training datasets. While simulated data is inexpensive to collect and annotate, it unfortunately does not always closely match the distribution of real-world data, which is known as the sim2real gap. In this paper we propose a novel method of synthetic data creation meant to close the sim2real gap for the challenging pedestrian detection task. Our method uses a diffusion-based architecture to learn a real-world distribution which, once trained, is used to generate datasets. We mix this generated data with simulated data as a form of augmentation and show that training on a combination of generated and simulated data increases average precision by as much as 27.3% for pedestrian detection models in real-world data, compared against training on purely simulated data.

该研究提出了一种使用扩散模型增强模拟数据集的方法，以改善行人检测在现实世界数据中的性能，并使用扩散基础架构来生成具有真实分布的数据集，以填补模拟数据和真实数据之间的差距，通过使用生成数据与模拟数据进行的训练可以将行人检测模型在真实数据中的平均精度提高27.3%。

扩散式数据集生成：缩小行人检测 Sim2Real 差距的步伐