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。

众包 NeRF：从生产车辆中收集数据进行 3D 街景重建

Crowd-Sourced NeRF: Collecting Data from Production Vehicles for 3D  Street View Reconstruction

While NeRF has shown great success for neural reconstruction and rendering,
its limited MLP capacity and long per-scene optimization times make it
challenging to model large-scale indoor scenes. In contrast, classical 3D
reconstruction methods can handle large-scale scenes but do not produce
realistic renderings. We propose NeRFusion, a method that combines the
advantages of NeRF and TSDF-based fusion techniques to achieve efficient
large-scale reconstruction and photo-realistic rendering. We process the input
image sequence to predict per-frame local radiance fields via direct network
inference. These are then fused using a novel recurrent neural network that
incrementally reconstructs a global, sparse scene representation in real-time
at 22 fps. This global volume can be further fine-tuned to boost rendering
quality. We demonstrate that NeRFusion achieves state-of-the-art quality on
both large-scale indoor and small-scale object scenes, with substantially
faster reconstruction than NeRF and other recent methods.

NeRFusion 提出了一种方法，通过将 NeRF 和 TSDF-based fusion 技术结合起来，实现了大规模室内场景的高效重建和逼真渲染，使用全新的循环神经网络来递增地重建实时稀疏场景表示，进一步提高了渲染质量，取得了大规模室内和小型物体场景的最先进质量，并比 NeRF 和其他最新方法重建速度更快。

NeRFusion: 大规模场景重建中径流场融合

NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction

View-graph is an essential input to large-scale structure from motion (SfM)
pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on
the input view-graph. Inconsistent or inaccurate edges can lead to inferior or
wrong reconstruction. Most SfM methods remove `undesirable' images and pairs
using several, fixed heuristic criteria, and propose tailor-made solutions to
achieve specific reconstruction objectives such as efficiency, accuracy, or
disambiguation. In contrast to these disparate solutions, we propose a single
optimization framework that can be used to achieve these different
reconstruction objectives with task-specific cost modeling. We also construct a
very efficient network-flow based formulation for its approximate solution. The
abstraction brought on by this selection mechanism separates the challenges
specific to datasets and reconstruction objectives from the standard SfM
pipeline and improves its generalization. This paper demonstrates the
application of the proposed view-graph framework with standard SfM pipeline for
two particular use-cases, (i) accurate and ghost-free reconstructions of highly
ambiguous datasets using costs based on disambiguation priors, and (ii)
accurate and efficient reconstruction of large-scale Internet datasets using
costs based on commonly used priors.

本研究提出了一种基于优化框架和网络流算法的视图图优化框架，可用于大规模运动结构三维重建，并通过成本模型实现了高精度、高效、准确重建的任务目标，该模型可以抽象出数据集和重建目标中独特的挑战，并显著改善了通用 SfM 管道的表现。

用于运动结构的统一视图图选择框架

A Unified View-Graph Selection Framework for Structure from Motion

Accurate signal recovery or image reconstruction from indirect and possibly
undersampled data is a topic of considerable interest; for example, the
literature in the recent field of compressed sensing is already quite immense.
Inspired by recent breakthroughs in the development of novel first-order
methods in convex optimization, most notably Nesterov's smoothing technique,
this paper introduces a fast and accurate algorithm for solving common recovery
problems in signal processing. In the spirit of Nesterov's work, one of the key
ideas of this algorithm is a subtle averaging of sequences of iterates, which
has been shown to improve the convergence properties of standard
gradient-descent algorithms. This paper demonstrates that this approach is
ideally suited for solving large-scale compressed sensing reconstruction
problems as 1) it is computationally efficient, 2) it is accurate and returns
solutions with several correct digits, 3) it is flexible and amenable to many
kinds of reconstruction problems, and 4) it is robust in the sense that its
excellent performance across a wide range of problems does not depend on the
fine tuning of several parameters. Comprehensive numerical experiments on
realistic signals exhibiting a large dynamic range show that this algorithm
compares favorably with recently proposed state-of-the-art methods. We also
apply the algorithm to solve other problems for which there are fewer
alternatives, such as total-variation minimization, and convex programs seeking
to minimize the l1 norm of Wx under constraints, in which W is not diagonal.

本文介绍了一种基于凸优化中 Nesterov 技术的高精密快速算法，用于解决信号处理中的常见恢复问题，包括大规模压缩感知重建、全变差最小化等，并通过实验验证了其优越性。