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
this https URL .

通过在自动驾驶场景中使用来自 LiDAR 的几何先验知识，Lightning NeRF 显著提高了 NeRF 的新视图合成性能，减少了计算开销，并在真实世界数据集上的评估中取得了显著的性能提升。