Generating complete 360-degree panoramas from narrow field of view images is
ongoing research as omnidirectional RGB data is not readily available. Existing
GAN-based approaches face some barriers to achieving higher quality output, and
have poor generalization performance over different mask types. In this paper,
we present our 360-degree indoor RGB panorama outpainting model using latent
diffusion models (LDM), called IPO-LDM. We introduce a new bi-modal latent
diffusion structure that utilizes both RGB and depth panoramic data during
training, but works surprisingly well to outpaint normal depth-free RGB images
during inference. We further propose a novel technique of introducing
progressive camera rotations during each diffusion denoising step, which leads
to substantial improvement in achieving panorama wraparound consistency.
Results show that our IPO-LDM not only significantly outperforms
state-of-the-art methods on RGB panorama outpainting, but can also produce
multiple and diverse well-structured results for different types of masks.

360 度室内 RGB 全景图像的生成研究中，使用有限的视野图像存在困难，而现有的基于 GAN 的方法面临着提高输出质量和在不同掩码类型下泛化性能较差的障碍。本文提出了一种使用潜在扩散模型（LDM）的 360 度室内 RGB 全景图像外扩模型 IPO-LDM。我们引入了一种新颖的双模潜在扩散结构，在训练过程中利用 RGB 和深度全景数据，但在推理过程中却能出色地对缺少深度信息的 RGB 图像进行外扩。此外，我们还提出了一种新颖的技术，在每个扩散去噪步骤中引入渐进式相机旋转，从而大幅改善全景图的一致性。实验结果表明，我们的 IPO-LDM 不仅在 RGB 全景图像外扩方面显著优于现有方法，还能为不同类型的掩码生成多个多样且结构良好的结果。