# Generative Adversarial Frontal View to Bird View Synthesis

arXiv: https://arxiv.org/abs/1808.00327 · HTML: https://mlsanity.com/paper/1808.00327/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2018.

## Built on by (most cited first, 25 of 25)

- [FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras](https://mlsanity.com/paper/2104.10490/) (2021)
- [NEAT: Neural Attention Fields for End-to-End Autonomous Driving](https://mlsanity.com/paper/2109.04456/) (2021)
- [Vision-Centric BEV Perception: A Survey](https://mlsanity.com/paper/2208.02797/) (2022)
- [Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization](https://mlsanity.com/paper/2103.06818/) (2021)
- [Bird's-Eye-View Panoptic Segmentation Using Monocular Frontal View Images](https://mlsanity.com/paper/2108.03227/) (2021)
- [InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning](https://mlsanity.com/paper/2301.04470/) (2023)
- [LaRa: Latents and Rays for Multi-Camera Bird's-Eye-View Semantic Segmentation](https://mlsanity.com/paper/2206.13294/) (2022)
- [GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation](https://mlsanity.com/paper/2204.07733/) (2022)
- [Learning by Watching](https://mlsanity.com/paper/2106.05966/) (2021)
- [LEDiff: Latent Exposure Diffusion for HDR Generation](https://mlsanity.com/paper/2412.14456/) (2024)
- [Cascaded Cross MLP-Mixer GANs for Cross-View Image Translation](https://mlsanity.com/paper/2110.10183/) (2021)
- [VLM-E2E: Enhancing End-to-End Autonomous Driving with Multimodal Driver Attention Fusion](https://mlsanity.com/paper/2502.18042/) (2025)
- [F2BEV: Bird's Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving](https://mlsanity.com/paper/2303.03651/) (2023)
- [A Cross-Scale Hierarchical Transformer with Correspondence-Augmented Attention for inferring Bird's-Eye-View Semantic Segmentation](https://mlsanity.com/paper/2304.03650/) (2023)
- [View Birdification in the Crowd: Ground-Plane Localization from Perceived Movements](https://mlsanity.com/paper/2111.05060/) (2021)
- [Monocular BEV Perception of Road Scenes via Front-to-Top View Projection](https://mlsanity.com/paper/2211.08144/) (2022)
- [Novel View Synthesis from a Single RGBD Image for Indoor Scenes](https://mlsanity.com/paper/2311.01065/) (2023)
- [Light Field Diffusion for Single-View Novel View Synthesis](https://mlsanity.com/paper/2309.11525/) (2023)
- [DaF-BEVSeg: Distortion-aware Fisheye Camera based Bird's Eye View Segmentation with Occlusion Reasoning](https://mlsanity.com/paper/2404.06352/) (2024)
- [Semantic RGB-D Image Synthesis](https://mlsanity.com/paper/2308.11356/) (2023)
- [BlindSpotNet: Seeing Where We Cannot See](https://mlsanity.com/paper/2207.03870/) (2022)
- [Survey on Controlable Image Synthesis with Deep Learning](https://mlsanity.com/paper/2307.10275/) (2023)
- [Deep Perspective Transformation Based Vehicle Localization on Bird's Eye View](https://mlsanity.com/paper/2311.06796/) (2023)
- [Bird Eye-View to Street-View: A Survey](https://mlsanity.com/paper/2405.08961/) (2024)
- [From Satellite to Street: A Hybrid Framework Integrating Stable Diffusion and PanoGAN for Consistent Cross-View Synthesis](https://mlsanity.com/paper/2509.24369/) (2025)

## Abstract (arXiv)

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.