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 pre-training 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.

翻译：神经辐射场（NeRF）已成为推动自动驾驶研究的重要工具，可提供可扩展的闭环仿真与数据增强能力。然而，若要信任仿真结果，必须确保自动驾驶系统能以相同方式感知真实数据与渲染数据。尽管渲染方法的性能不断提升，但许多场景本质上仍难以实现高保真重建。为此，我们提出了解决真实-仿真数据鸿沟的新视角：不再单纯追求渲染精度的提升，而是探索简单有效的方法，在不牺牲真实数据性能的前提下增强感知模型对NeRF伪影的鲁棒性。此外，我们首次利用最先进的神经渲染技术，在自动驾驶场景中开展真实-仿真数据鸿沟的大规模研究。具体而言，我们在真实与仿真数据上评估了目标检测器与在线建图模型，并分析了不同预训练策略的影响。结果表明，模型对仿真数据的鲁棒性显著提升，某些情况下甚至改善了真实世界的性能。最后，我们深入探究了真实-仿真数据鸿沟与图像重建指标之间的相关性，发现FID和LPIPS是强有力的指示因子。