Are NeRFs Ready for Autonomous Driving? Towards Closing the Real-to-simulation Gap

Carl Lindström, Georg Hess, Adam Lilja, Maryam Fatemi, Lars Hammarstrand, Christoffer Petersson, Lennart Svensson; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4461-4471

Neural Radiance Fields (NeRFs) have emerged as promising tools for advancing autonomous driving (AD) research offering scalable closed-loop simulation and data augmentation capabilities. However to trust the results achieved in simulation one needs to ensure that AD systems perceive real and rendered data in the same way. Although the performance of rendering methods is increasing many scenarios will remain inherently challenging to reconstruct faithfully. To this end we propose a novel perspective for addressing the real-to-simulated data gap. Rather than solely focusing on improving rendering fidelity we explore simple yet effective methods to enhance perception model robustness to NeRF artifacts without compromising performance on real data. Moreover we conduct the first large-scale investigation into the real-to-simulated data gap in an AD setting using a state-of-the-art neural rendering technique. Specifically we evaluate object detectors and an online mapping model on real and simulated data and study the effects of different fine-tuning strategies. Our results show notable improvements in model robustness to simulated data even improving real-world performance in some cases. Last we delve into the correlation between the real-to-simulated gap and image reconstruction metrics identifying FID and LPIPS as strong indicators.