Recent studies have highlighted the promising application of NeRF in autonomous driving contexts. However, the complexity of outdoor environments, combined with the restricted viewpoints in driving scenarios, complicates the task of precisely reconstructing scene geometry. Such challenges often lead to diminished quality in reconstructions and extended durations for both training and rendering. To tackle these challenges, we present Lightning NeRF. It uses an efficient hybrid scene representation that effectively utilizes the geometry prior from LiDAR in autonomous driving scenarios. Lightning NeRF significantly improves the novel view synthesis performance of NeRF and reduces computational overheads. Through evaluations on real-world datasets, such as KITTI-360, Argoverse2, and our private dataset, we demonstrate that our approach not only exceeds the current state-of-the-art in novel view synthesis quality but also achieves a five-fold increase in training speed and a ten-fold improvement in rendering speed. Codes are available at https://github.com/VISION-SJTU/Lightning-NeRF .

翻译：近期研究凸显了NeRF在自动驾驶场景中的潜力。然而，户外环境的复杂性与驾驶场景中的受限视角相结合，使得精确重建场景几何的任务变得复杂。此类挑战常导致重建质量下降，并延长训练与渲染耗时。为解决这些问题，我们提出闪电NeRF（Lightning NeRF）。该方法采用高效混合场景表示，有效利用了自动驾驶场景中LiDAR的几何先验信息。闪电NeRF显著提升了NeRF的新视角合成性能，并降低了计算开销。通过在KITTI-360、Argoverse2等真实数据集及我们私有数据集上的评估，验证了本方法不仅在新视角合成质量上超越当前最优水平，更实现了五倍的训练速度提升与十倍的渲染效率改进。代码已开源至https://github.com/VISION-SJTU/Lightning-NeRF。