# Geometry-Guided Street-View Panorama Synthesis from Satellite Imagery

arXiv: https://arxiv.org/abs/2103.01623 · HTML: https://mlsanity.com/paper/2103.01623/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 2 Mar 2021.

## Builds on (most cited first, 21 of 21)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://mlsanity.com/paper/1602.07360/) (2016)
- [Single-View View Synthesis with Multiplane Images](https://mlsanity.com/paper/2004.11364/) (2020)
- [DeepView: View Synthesis with Learned Gradient Descent](https://mlsanity.com/paper/1906.07316/) (2019)
- [Wide-Area Image Geolocalization with Aerial Reference Imagery](https://mlsanity.com/paper/1510.03743/) (2015)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching](https://mlsanity.com/paper/2005.03860/) (2020)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation](https://mlsanity.com/paper/1904.06807/) (2019)
- [Bridging the Domain Gap for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/1904.11045/) (2019)
- [Localizing and Orienting Street Views Using Overhead Imagery](https://mlsanity.com/paper/1608.00161/) (2016)
- [Optimal Feature Transport for Cross-View Image Geo-Localization](https://mlsanity.com/paper/1907.05021/) (2019)
- [Predicting Ground-Level Scene Layout from Aerial Imagery](https://mlsanity.com/paper/1612.02709/) (2016)
- [Cross-View Image Synthesis using Conditional GANs](https://mlsanity.com/paper/1803.03396/) (2018)
- [Cross-view image synthesis using geometry-guided conditional GANs](https://mlsanity.com/paper/1808.05469/) (2018)
- [Light-Weight RefineNet for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1810.03272/) (2018)
- [Geometry-aware Deep Network for Single-Image Novel View Synthesis](https://mlsanity.com/paper/1804.06008/) (2018)

## Built on by (most cited first, 30 of 41)

- [Street-View Image Generation from a Bird's-Eye View Layout](https://mlsanity.com/paper/2301.04634/) (2023)
- [Beyond Cross-view Image Retrieval: Highly Accurate Vehicle Localization Using Satellite Image](https://mlsanity.com/paper/2204.04752/) (2022)
- [SliceMatch: Geometry-guided Aggregation for Cross-View Pose Estimation](https://mlsanity.com/paper/2211.14651/) (2022)
- [Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer](https://mlsanity.com/paper/2307.08015/) (2023)
- [Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs](https://mlsanity.com/paper/2303.14672/) (2023)
- [Sat2Scene: 3D Urban Scene Generation from Satellite Images with Diffusion](https://mlsanity.com/paper/2401.10786/) (2024)
- [CVLNet: Cross-View Semantic Correspondence Learning for Video-based Camera Localization](https://mlsanity.com/paper/2208.03660/) (2022)
- [CrossViewDiff: A Cross-View Diffusion Model for Satellite-to-Street View Synthesis](https://mlsanity.com/paper/2408.14765/) (2024)
- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery](https://mlsanity.com/paper/2510.15869/) (2025)
- [Sat2City: 3D City Generation from A Single Satellite Image with Cascaded Latent Diffusion](https://mlsanity.com/paper/2507.04403/) (2025)
- [Self-Supervised Visibility Learning for Novel View Synthesis](https://mlsanity.com/paper/2103.15407/) (2021)
- [Visual and Object Geo-localization: A Comprehensive Survey](https://mlsanity.com/paper/2112.15202/) (2021)
- [Revisiting Near/Remote Sensing with Geospatial Attention](https://mlsanity.com/paper/2204.01807/) (2022)
- [Leveraging BEV Paradigm for Ground-to-Aerial Image Synthesis](https://mlsanity.com/paper/2408.01812/) (2024)
- [Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance](https://mlsanity.com/paper/2408.04224/) (2024)
- [VAGeo: View-specific Attention for Cross-View Object Geo-Localization](https://mlsanity.com/paper/2501.07194/) (2025)
- [Seeing through Satellite Images at Street Views](https://mlsanity.com/paper/2505.17001/) (2025)
- [Sat2RealCity: Geometry-Aware and Appearance-Controllable 3D Urban Generation from Satellite Imagery](https://mlsanity.com/paper/2511.11470/) (2025)
- [OmniSyn: Synthesizing 360 Videos with Wide-baseline Panoramas](https://mlsanity.com/paper/2202.08752/) (2022)
- [Geometry-guided Cross-view Diffusion for One-to-many Cross-view Image Synthesis](https://mlsanity.com/paper/2412.03315/) (2024)
- [Sat3DGen: Comprehensive Street-Level 3D Scene Generation from Single Satellite Image](https://mlsanity.com/paper/2605.14984/) (2026)
- [Aerial Diffusion: Text Guided Ground-to-Aerial View Translation from a Single Image using Diffusion Models](https://mlsanity.com/paper/2303.11444/) (2023)
- [Mixed-View Panorama Synthesis using Geospatially Guided Diffusion](https://mlsanity.com/paper/2407.09672/) (2024)
- [Scale-adaptive UAV Geo-localization via Height-aware Partition Learning](https://mlsanity.com/paper/2412.11535/) (2024)
- [Enhancing Monocular Height Estimation from Aerial Images with Street-view Images](https://mlsanity.com/paper/2311.02121/) (2023)
- [SA-Occ: Satellite-Assisted 3D Occupancy Prediction in Real World](https://mlsanity.com/paper/2503.16399/) (2025)
- [Geo2: Geometry-Guided Cross-view Geo-Localization and Image Synthesis](https://mlsanity.com/paper/2603.25819/) (2026)
- [Sat2City v2: Native 3D City Asset Generation from a Single Satellite Image](https://mlsanity.com/paper/2606.24138/) (2026)
- [Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings](https://mlsanity.com/paper/2412.11529/) (2024)

## Abstract (arXiv)

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 information of this 3D point. Motivated by this, we develop a novel Satellite to Street-view image Projection (S2SP) module which explicitly establishes such geometric correspondences and projects the satellite images to the street viewpoint. With these projected satellite images as network input, we next employ a generator to synthesize realistic street-view panoramas that are geometrically consistent with the satellite images. Our S2SP module is differentiable and the whole framework is trained in an end-to-end manner. Extensive experimental results on two cross-view benchmark datasets demonstrate that our method generates images that better respect the scene geometry than existing approaches.