Summary of Aerialgo: Walking-through City View Generation From Aerial Perspectives, by Fuqiang Zhao et al.
AerialGo: Walking-through City View Generation from Aerial Perspectives
by Fuqiang Zhao, Yijing Guo, Siyuan Yang, Xi Chen, Luo Wang, Lan Xu, Yingliang Zhang, Yujiao Shi, Jingyi Yu
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework called AerialGo is proposed for generating realistic walking-through city views from aerial images, without the need for direct ground-level data collection. This approach leverages multi-view diffusion models to achieve photorealistic urban reconstructions while bypassing privacy risks associated with sensitive information such as vehicle plates and faces. The framework conditions ground-view synthesis on accessible aerial data, enabling scalable and realistic city-scale 3D modeling. AerialGo dataset is introduced, containing paired aerial and ground-view images, camera and depth information, designed to support generative urban reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to create detailed maps of cities from the air has been developed. This approach uses computer models to generate views of streets and buildings without needing to collect data on the ground. This is helpful because it avoids showing personal information like license plates and faces. The system uses aerial images as a starting point and adds details from what’s visible on the ground. It can create very realistic maps that are useful for city planning, navigation, and virtual reality. |
Keywords
» Artificial intelligence » Diffusion
