In this study, we present a method for synthesizing novel views from a single
360-degree RGB-D image based on the neural radiance field (NeRF) . Prior
studies relied on the neighborhood interpolation capability of multi-layer
perceptrons to complete missing regions caused by occlusion and zooming, which
leads to artifacts. In the method proposed in this study, the input image is
reprojected to 360-degree RGB images at other camera positions, the missing
regions of the reprojected images are completed by a 2D image generative model,
and the completed images are utilized to train the NeRF. Because multiple
completed images contain inconsistencies in 3D, we introduce a method to learn
the NeRF model using a subset of completed images that cover the target scene
with less overlap of completed regions. The selection of such a subset of
images can be attributed to the maximum weight independent set problem, which
is solved through simulated annealing. Experiments demonstrated that the
proposed method can synthesize plausible novel views while preserving the
features of the scene for both artificial and real-world data.

本文介绍了一种基于神经辐射场（NeRF）的方法，用于从单个 360 度 RGB-D 图像合成新景象，并利用 2D 图像生成模型，最大权重独立集问题和模拟退火等算法，以优化生成的图像。

利用全向图像完善技术提高新视角合成质量

Enhancement of Novel View Synthesis Using Omnidirectional Image  Completion

One powerful technique to solve NP-hard optimization problems in practice is
branch-and-reduce search---which is branch-and-bound that intermixes branching
with reductions to decrease the input size. While this technique is known to be
very effective in practice for unweighted problems, very little is known for
weighted problems, in part due to a lack of known effective reductions. In this
work, we develop a full suite of new reductions for the maximum weight
independent set problem and provide extensive experiments to show their
effectiveness in practice on real-world graphs of up to millions of vertices
and edges.
Our experiments indicate that our approach is able to outperform existing
state-of-the-art algorithms, solving many instances that were previously
infeasible. In particular, we show that branch-and-reduce is able to solve a
large number of instances up to two orders of magnitude faster than existing
(inexact) local search algorithms---and is able to solve the majority of
instances within 15 minutes. For those instances remaining infeasible, we show
that combining kernelization with local search produces higher-quality
solutions than local search alone.

本文提出了一种新的最大权独立集问题分支和缩减策略，结合核心化与局部搜索能够比局部搜索单独得出更高质量的解，通过实验表明，在实践中这种方法比现有的算法更加有效，可以处理高达数百万个顶点和边的实际问题。