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.

翻译：大规模三维场景重建与新视角合成对自动驾驶至关重要，特别是利用时间上稀疏的激光雷达帧。然而，传统显式表示方法在实现无限分辨率的重建与合成场景方面仍存在显著瓶颈。尽管近期发展的神经辐射场（NeRF）在隐式表示领域展现出令人瞩目的成果，但基于稀疏激光雷达帧的大规模三维场景重建与新视角合成问题仍未被充分探索。为弥补这一空白，我们提出了一种名为父子神经辐射场（PC-NeRF）的三维场景重建与新视角合成框架。该框架基于父NeRF和子NeRF两个模块，实现了层次化空间划分与多层级场景表示（涵盖场景、片段和点级别）。多层级场景表示提升了稀疏激光雷达点云数据的利用效率，并能快速获取近似的体积场景表示。大量实验证明，PC-NeRF能够在大型场景中实现高精度激光雷达新视角合成与三维重建。此外，PC-NeRF可有效处理稀疏激光雷达帧的情况，并在有限训练轮次下展现出高部署效率。我们的方法实现与预训练模型已开源至 https://github.com/biter0088/pc-nerf。