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 this https URL

借助父 - 子神经辐射场（PC-NeRF）框架，通过利用部分丢失的传感器数据，实现大规模三维场景的高精度重建，并保证了解决部分传感器数据丢失情况下的高部署效率。

PC-NeRF：自动驾驶环境下局部传感器数据丢失的父子神经光线场模型

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

We propose a probabilistic shape completion method extended to the continuous
geometry of large-scale 3D scenes. Real-world scans of 3D scenes suffer from a
considerable amount of missing data cluttered with unsegmented objects. The
problem of shape completion is inherently ill-posed, and high-quality result
requires scalable solutions that consider multiple possible outcomes. We employ
the Generative Cellular Automata that learns the multi-modal distribution and
transform the formulation to process large-scale continuous geometry. The local
continuous shape is incrementally generated as a sparse voxel embedding, which
contains the latent code for each occupied cell. We formally derive that our
training objective for the sparse voxel embedding maximizes the variational
lower bound of the complete shape distribution and therefore our progressive
generation constitutes a valid generative model. Experiments show that our
model successfully generates diverse plausible scenes faithful to the input,
especially when the input suffers from a significant amount of missing data. We
also demonstrate that our approach outperforms deterministic models even in
less ambiguous cases with a small amount of missing data, which infers that
probabilistic formulation is crucial for high-quality geometry completion on
input scans exhibiting any levels of completeness.

该研究提出了一种面向大规模 3D 场景连续几何的概率形状完成方法，该方法利用生成细胞自动机学习多模态分布，通过稀疏体素嵌入渐进式生成连续形状，其训练目标最大化完整形状分布的变分下限，并通过实验证明了相比于确定性模型，该方法在几何完成方面的表现更佳。