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 to generate 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 guiding 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. The supplementary video can be viewed at https://youtu.be/jEQWr-Rfh3A.

翻译：模拟摄像头传感器是自动驾驶中的关键任务。尽管神经辐射场在驾驶仿真中生成逼真视图方面表现卓越，但仍难以生成外推视角。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。核心思想在于，地图信息可作为带不确定性的先验引导辐射场训练。具体而言，我们利用粗糙地面表面作为不确定性信息监督密度场，并通过未知相机位姿引入深度不确定性来扭曲深度，确保多视图一致性。实验结果表明，我们的方法能在车辆摄像头仿真的偏离视图中生成语义一致性。补充视频可于 https://youtu.be/jEQWr-Rfh3A 观看。