Geometry-Guided Street-View Panorama Synthesis from Satellite Imagery
Yujiao Shi, Dylan Campbell, Xin Yu, Hongdong Li

TL;DR
This paper introduces a geometry-guided method for synthesizing realistic street-view panoramas from satellite images by explicitly modeling geometric correspondences, improving the spatial consistency of generated images.
Contribution
It proposes a novel Satellite to Street-view image Projection (S2SP) module that explicitly encodes geometric relations, enhancing cross-view synthesis accuracy.
Findings
Outperforms existing methods on benchmark datasets.
Produces geometrically consistent street-view panoramas.
The S2SP module is differentiable and end-to-end trainable.
Abstract
This paper presents a new approach for synthesizing a novel street-view panorama given an overhead satellite image. Taking a small satellite image patch as input, our method generates a Google's omnidirectional street-view type panorama, as if it is captured from the same geographical location as the center of the satellite patch. Existing works tackle this task as an image generation problem which adopts generative adversarial networks to implicitly learn the cross-view transformations, while ignoring the domain relevance. In this paper, we propose to explicitly establish the geometric correspondences between the two-view images so as to facilitate the cross-view transformation learning. Specifically, we observe that when a 3D point in the real world is visible in both views, there is a deterministic mapping between the projected points in the two-view images given the height…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Advanced Vision and Imaging · Image Processing and 3D Reconstruction
