PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data
  Loss in Autonomous Driving Environments
Xiuzhong Hu Guangming Xiong Zheng Zang Peng Jia Yuxuan Han Junyi Ma
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.