Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation, enabling testing of AD systems, and as an advanced training data augmentation technique. However, existing methods often require long training times, dense semantic supervision, or lack generalizability. This, in turn, hinders the application of NeRFs for AD at scale. In this paper, we propose NeuRAD, a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design, extensive sensor modeling for both camera and lidar -- including rolling shutter, beam divergence and ray dropping -- and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets, achieving state-of-the-art performance across the board. To encourage further development, we will openly release the NeuRAD source code. See https://github.com/georghess/NeuRAD .

翻译：神经辐射场（NeRF）在自动驾驶（AD）领域日益受到关注。最新研究表明，NeRF在闭环仿真（支持自动驾驶系统测试）和先进训练数据增强技术方面具有潜力。然而现有方法通常需要较长的训练时间、密集的语义监督或缺乏泛化能力，这阻碍了NeRF在自动驾驶规模化应用中的推广。本文提出NeuRAD——一种专为动态自动驾驶数据设计的鲁棒新视角合成方法。该方法具有简洁的网络结构，包含卷帘快门、光束发散和射线丢弃在内的完整相机与激光雷达传感器建模，且可直接适用于多种数据集。我们在五个主流自动驾驶数据集上验证其性能，全面达到当前最优水平。为促进该领域发展，我们将公开NeuRAD源代码（详见 https://github.com/georghess/NeuRAD ）。