Large-scale 3D scene reconstruction and novel view synthesis are vital for autonomous vehicles, especially utilizing temporally sparse LiDAR frames. However, conventional explicit representations remain a significant bottleneck towards representing the reconstructed and synthetic scenes at unlimited resolution. Although the recently developed neural radiance fields (NeRF) have shown compelling results in implicit representations, the problem of large-scale 3D scene reconstruction and novel view synthesis using sparse LiDAR frames remains unexplored. To bridge this gap, we propose a 3D scene reconstruction and novel view synthesis framework called parent-child neural radiance field (PC-NeRF). Based on its two modules, parent NeRF and child NeRF, the framework implements hierarchical spatial partitioning and multi-level scene representation, including scene, segment, and point levels. The multi-level scene representation enhances the efficient utilization of sparse LiDAR point cloud data and enables the rapid acquisition of an approximate volumetric scene representation. With extensive experiments, PC-NeRF is proven to achieve high-precision novel LiDAR view synthesis and 3D reconstruction in large-scale scenes. Moreover, PC-NeRF can effectively handle situations with sparse LiDAR frames and demonstrate high deployment efficiency with limited training epochs. Our approach implementation and the pre-trained models are available at https://github.com/biter0088/pc-nerf.

提出了一种称为PC-NeRF的3D场景重建和新视角综合框架，通过两个模块实现层次化空间划分和多级场景表示，包括场景、段和点级别。这种多级场景表示增强了稀疏LiDAR点云数据的高效利用和快速获取近似的体积场景表示。通过广泛的实验证明，PC-NeRF可以在大规模场景中实现高精度的新LiDAR视角综合和3D重建，并能够有效处理稀疏的LiDAR帧并在有限训练迭代次数下展示高部署效率。

PC-NeRF：在自动驾驶环境中使用稀疏LiDAR帧的父子神经辐射场