  Environment perception is an important task with great practical value and
bird view is an essential part for creating panoramas of surrounding
environment. Due to the large gap and severe deformation between the frontal
view and bird view, generating a bird view image from a single frontal view is
challenging. To tackle this problem, we propose the BridgeGAN, i.e., a novel
generative model for bird view synthesis. First, an intermediate view, i.e.,
homography view, is introduced to bridge the large gap. Next, conditioned on
the three views (frontal view, homography view and bird view) in our task, a
multi-GAN based model is proposed to learn the challenging cross-view
translation. Extensive experiments conducted on a synthetic dataset have
demonstrated that the images generated by our model are much better than those
generated by existing methods, with more consistent global appearance and
sharper details. Ablation studies and discussions show its reliability and
robustness in some challenging cases.

Computer Vision and Pattern Recognition