We introduce the task of mixed-view panorama synthesis, where the goal is to synthesize a novel panorama given a small set of input panoramas and a satellite image of the area. This contrasts with previous work which only uses input panoramas (same-view synthesis), or an input satellite image (cross-view synthesis). We argue that the mixed-view setting is the most natural to support panorama synthesis for arbitrary locations worldwide. A critical challenge is that the spatial coverage of panoramas is uneven, with few panoramas available in many regions of the world. We introduce an approach that utilizes diffusion-based modeling and an attention-based architecture for extracting information from all available input imagery. Experimental results demonstrate the effectiveness of our proposed method. In particular, our model can handle scenarios when the available panoramas are sparse or far from the location of the panorama we are attempting to synthesize.

我们引入了混合视图全景合成的任务，其目标是在给定一小组输入全景图和一幅卫星图像的情况下合成新的全景图。通过采用基于扩散的建模和基于注意力的架构，我们的提议方法可以从所有可用的输入图像中提取信息，并成功处理当可用全景图稀缺或远离我们尝试合成的全景图位置的场景。

地理引导扩散的混合视角全景合成