Lightning NeRF: Efficient Hybrid Scene Representation for Autonomous
  Driving
Junyi Cao Zhichao Li Naiyan Wang Chao Ma
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 .