Summary of Application Of Disentanglement to Map Registration Problem, by Hae Jin Song et al.
Application of Disentanglement to Map Registration Problem
by Hae Jin Song, Patrycja Krawczuk, Po-Hsuan Huang
First submitted to arxiv on: 26 Aug 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 The proposed approach for image registration in geospatial data analysis involves a two-step process. First, it extracts geospatial contents that are invariant to visual and other non-content-related information, and then matches the data based on these purely geospatial contents using a combination of -VAE-like architecture and adversarial training. This enables the disentanglement of geographic information and artistic styles, allowing for the generation of new map tiles by composing encoded geographic information with any artistic style. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Geospatial data come from different sources like satellites, aircraft, and LiDAR. The challenge is to align these data to make sense. This paper proposes a two-step process: extracting geospatial contents that are the same across different maps, then matching the data based on those contents. They use a special kind of AI model called -VAE-like architecture and training it in a way that separates geographic information from artistic styles. This can help create new map tiles by combining geographic information with any artistic style. |
