Recently, Neural Radiance Fields (NeRF) achieved impressive results in novel
view synthesis. Block-NeRF showed the capability of leveraging NeRF to build
large city-scale models. For large-scale modeling, a mass of image data is
necessary. Collecting images from specially designed data-collection vehicles
can not support large-scale applications. How to acquire massive high-quality
data remains an opening problem. Noting that the automotive industry has a huge
amount of image data, crowd-sourcing is a convenient way for large-scale data
collection. In this paper, we present a crowd-sourced framework, which utilizes
substantial data captured by production vehicles to reconstruct the scene with
the NeRF model. This approach solves the key problem of large-scale
reconstruction, that is where the data comes from and how to use them. Firstly,
the crowd-sourced massive data is filtered to remove redundancy and keep a
balanced distribution in terms of time and space. Then a structure-from-motion
module is performed to refine camera poses. Finally, images, as well as poses,
are used to train the NeRF model in a certain block. We highlight that we
present a comprehensive framework that integrates multiple modules, including
data selection, sparse 3D reconstruction, sequence appearance embedding, depth
supervision of ground surface, and occlusion completion. The complete system is
capable of effectively processing and reconstructing high-quality 3D scenes
from crowd-sourced data. Extensive quantitative and qualitative experiments
were conducted to validate the performance of our system. Moreover, we proposed
an application, named first-view navigation, which leveraged the NeRF model to
generate 3D street view and guide the driver with a synthesized video.

借助大规模众包数据的神经光辐射场（NeRF）重建框架，解决了大规模重建中的数据获取问题，并利用这些数据训练 NeRF 模型生成高质量的 3D 场景。