Reconstructing large-scale 3D scenes is essential for autonomous vehicles, especially when partial sensor data is lost. Although the recently developed neural radiance fields (NeRF) have shown compelling results in implicit representations, the large-scale 3D scene reconstruction using partially lost LiDAR point cloud data still needs to be explored. To bridge this gap, we propose a novel 3D scene reconstruction framework called parent-child neural radiance field (PC-NeRF). The framework comprises two modules, the parent NeRF and the child NeRF, to simultaneously optimize scene-level, segment-level, and point-level scene representations. Sensor data can be utilized more efficiently by leveraging the segment-level representation capabilities of child NeRFs, and an approximate volumetric representation of the scene can be quickly obtained even with limited observations. With extensive experiments, our proposed PC-NeRF is proven to achieve high-precision 3D reconstruction in large-scale scenes. Moreover, PC-NeRF can effectively tackle situations where partial sensor data is lost and has high deployment efficiency with limited training time. Our approach implementation and the pre-trained models will be available at https://github.com/biter0088/pc-nerf.

翻译：大规模三维场景重建对于自动驾驶车辆至关重要，尤其是在部分传感器数据丢失的情况下。尽管近年来发展的神经辐射场（NeRF）在隐式表示方面取得了显著成果，但基于部分丢失的激光雷达点云数据进行大规模三维场景重建仍有待探索。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）的新型三维场景重建框架。该框架包含父NeRF和子NeRF两个模块，可同时优化场景级、片段级和点级场景表示。通过利用子NeRF的片段级表示能力，传感器数据能够得到更高效的利用，即便在观测数据有限的条件下，也能快速获得场景的近似体积表示。大量实验证明，我们提出的PC-NeRF能够在大规模场景中实现高精度三维重建。此外，PC-NeRF能有效应对部分传感器数据丢失的情况，且在有限的训练时间内具有较高的部署效率。我们的实现方法和预训练模型将在https://github.com/biter0088/pc-nerf 上公开。