PC-NeRF: Parent-Child Neural Radiance Fields Using Sparse LiDAR Frames
  in Autonomous Driving Environments
Xiuzhong Hu Guangming Xiong Zheng Zang Peng Jia Yuxuan Han Junyi Ma
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.