3 papers
cs.CV2026
ASTAD: Asymmetric Style Transfer for Synthetic-to-Real Adaptation in Autonomous Driving
Dingyi Yao, Xinqi Zhang, Lihui Peng +3
Synthetic data mitigates the data scarcity problem in autonomous driving perception. However, the synthetic-to-real gap leads to performance degradation, hindering real-world model…
cs.CV2026
A Style-Based Profiling Framework for Quantifying the Synthetic-to-Real Gap in Autonomous Driving Datasets
Dingyi Yao, Xinyao Han, Ruibo Ming +5
Ensuring the reliability of autonomous driving perception systems requires extensive environment-based testing, yet real-world execution is often impractical. Synthetic datasets ha…
cs.CV2025
Synthetic Dataset Evaluation Based on Generalized Cross Validation
Zhihang Song, Dingyi Yao, Ruibo Ming +3
With the rapid advancement of synthetic dataset generation techniques, evaluating the quality of synthetic data has become a critical research focus. Robust evaluation not only dri…