Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection
This addresses the high cost of real-world data collection for pedestrian detection, though it is an incremental improvement over existing simulation methods.
The paper tackles the sim2real gap in pedestrian detection by augmenting simulated datasets with diffusion-generated data, resulting in up to a 27.3% increase in average precision on real-world data.
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.