Generative Adversarial Frontal View to Bird View Synthesis
This work addresses a domain-specific problem in environment perception for creating panoramas, but it is incremental as it builds on existing GAN-based methods with a new intermediate view approach.
The paper tackles the challenging problem of generating bird view images from a single frontal view by proposing BridgeGAN, a novel generative model that introduces an intermediate homography view to bridge the gap. Experiments on a synthetic dataset show the model outperforms existing methods with more consistent global appearance and sharper details.
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.