Simulating camera sensors is a crucial task in autonomous driving. Although neural radiance fields are exceptional at synthesizing photorealistic views in driving simulations, they still fail in generating extrapolated views. This paper proposes to incorporate map priors into neural radiance fields to synthesize out-of-trajectory driving views with semantic road consistency. The key insight is that map information can be utilized as a prior to guide the training of the radiance fields with uncertainty. Specifically, we utilize the coarse ground surface as uncertain information to supervise the density field and warp depth with uncertainty from unknown camera poses to ensure multi-view consistency. Experimental results demonstrate that our approach can produce semantic consistency in deviated views for vehicle camera simulation.

翻译：模拟摄像头传感器是自动驾驶中的一项关键任务。尽管神经辐射场在驾驶模拟中合成逼真视图方面表现出色，但在生成外推视图时仍存在不足。本文提出将地图先验融入神经辐射场，以合成具有语义道路一致性的轨迹外驾驶视图。其核心思想在于：地图信息可作为先验，通过不确定性引导辐射场的训练。具体而言，我们利用粗糙地面表面作为不确定信息来监督密度场，并通过未知相机位姿带来的不确定性对深度进行扭曲，以确保多视图一致性。实验结果表明，我们的方法能够在车辆摄像头模拟的偏离视角中产生语义一致性。