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.

翻译：本研究提出一种基于神经辐射场（NeRF）从单张360度RGB-D图像合成新视角的方法。以往研究依赖多层感知器的邻域插值能力来填补由遮挡和缩放引起的缺失区域，这会导致伪影产生。在本研究提出的方法中，输入图像被重投影至其他相机位置的360度RGB图像，通过二维图像生成模型补全重投影图像的缺失区域，并利用补全后的图像训练NeRF。由于多张补全图像存在三维不一致性，我们引入一种方法——通过选取覆盖目标场景且补全区域重叠较少的补全图像子集来学习NeRF模型。这种子集图像的选取可归结为最大权重独立集问题，并通过模拟退火算法求解。实验表明，所提方法在合成合理新视角的同时，能保留人工数据与真实世界数据的场景特征。