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。