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Reconstructing large-scale 3D scenes is essential for autonomous vehicles,especially when partial sensor data is lost. Although the recently developedneural radiance fields (NeRF) have shown compelling results in implicitrepresentations, the large-scale 3D scene reconstruction using partially lostLiDAR point cloud data still needs to be explored. To bridge this gap, wepropose a novel 3D scene reconstruction framework called parent-child neuralradiance field (PC-NeRF). The framework comprises two modules, the parent NeRFand the child NeRF, to simultaneously optimize scene-level, segment-level, andpoint-level scene representations. Sensor data can be utilized more efficientlyby leveraging the segment-level representation capabilities of child NeRFs, andan approximate volumetric representation of the scene can be quickly obtainedeven with limited observations. With extensive experiments, our proposedPC-NeRF is proven to achieve high-precision 3D reconstruction in large-scalescenes. Moreover, PC-NeRF can effectively tackle situations where partialsensor data is lost and has high deployment efficiency with limited trainingtime. Our approach implementation and the pre-trained models will be availableat https://github.com/biter0088/pc-nerf.

PC-NeRF: Parent-Child Neural Radiance Fields under Partial Sensor Data Loss in Autonomous Driving Environments