We address the problem of generating a 360-degree image from a single image
with a narrow field of view by estimating its surroundings. Previous methods
suffered from overfitting to the training resolution and deterministic
generation. This paper proposes a completion method using a transformer for
scene modeling and novel methods to improve the properties of a 360-degree
image on the output image. Specifically, we use CompletionNets with a
transformer to perform diverse completions and AdjustmentNet to match color,
stitching, and resolution with an input image, enabling inference at any
resolution. To improve the properties of a 360-degree image on an output image,
we also propose WS-perceptual loss and circular inference. Thorough experiments
show that our method outperforms state-of-the-art (SOTA) methods both
qualitatively and quantitatively. For example, compared to SOTA methods, our
method completes images 16 times larger in resolution and achieves 1.7 times
lower Frechet inception distance (FID). Furthermore, we propose a pipeline that
uses the completion results for lighting and background of 3DCG scenes. Our
plausible background completion enables perceptually natural results in the
application of inserting virtual objects with specular surfaces.

本文提出了基于 Transformers 和 CompletionNets 的自动场景建模方法，使用 WS-perceptual loss 和 circular inference 提升 360 度图像的质量，并在虚拟物体插入场景上取得了具有感知自然性的结果。