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）在自动驾驶领域已获得广泛关注。近期方法展示了NeRF在闭环仿真（支持自动驾驶系统测试）及高级训练数据增强中的潜力。然而现有方法往往需要长时训练、密集语义监督或缺乏泛化能力，这阻碍了NeRF在自动驾驶中的规模化应用。本文提出NeuRAD——一种针对动态自动驾驶数据定制的鲁棒新颖视图合成方法。该方法采用简约网络设计，集成针对相机与激光雷达的全面传感器建模（包括卷帘快门、光束发散与光线缺失），且可开箱即用于多数据集。我们在五个主流自动驾驶数据集上验证其性能，均取得当前最佳结果。为鼓励后续研究，我们将开放NeuRAD源代码（详见https://github.com/georghess/NeuRAD）。