Summary of Unsupervised Homography Estimation on Multimodal Image Pair Via Alternating Optimization, by Sanghyeob Song et al.
Unsupervised Homography Estimation on Multimodal Image Pair via Alternating Optimization
by Sanghyeob Song, Jaihyun Lew, Hyemi Jang, Sungroh Yoon
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 AltO framework is an unsupervised learning method for estimating homography in multimodal image pairs, a crucial task for mid- or high-level vision tasks like image stitching and fusion. The approach employs a two-phase alternating optimization framework, similar to Expectation-Maximization (EM), which reduces the geometry gap and addresses the modality gap using Barlow Twins loss and an extended version, Geometry Barlow Twins. This method can be trained on multimodal datasets without ground-truth data, outperforming other unsupervised methods and being compatible with various architectures of homography estimators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AltO is a new way to match images together from different places or times. It’s like trying to figure out how to put two puzzle pieces together even if they look very different. Right now, it’s hard to make computers do this because we need lots of examples of what the correct answer should be. But AltO can do it without needing those examples. This means it can help us make better maps, combine pictures from different cameras, and even improve how robots see their surroundings. |
Keywords
» Artificial intelligence » Optimization » Unsupervised
