Summary of Map-relative Pose Regression For Visual Re-localization, by Shuai Chen et al.
Map-Relative Pose Regression for Visual Re-Localization
by Shuai Chen, Tommaso Cavallari, Victor Adrian Prisacariu, Eric Brachmann
First submitted to arxiv on: 15 Apr 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 This paper proposes a new approach to camera pose regression, map-relative pose regression (marepo), which can be trained across hundreds of scenes and fine-tuned in mere minutes for high accuracy. Building on the success of absolute pose regression (APR) networks, marepo encodes scene geometry implicitly in its weights but conditions the pose regressor on a scene-specific map representation. This allows for scene-agnostic training and immediate application to new map representations. The approach outperforms previous pose regression methods by far on two public datasets, indoor and outdoor. Code is available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us take better photos of our surroundings! It’s about finding the right angle to capture a scene, like taking a selfie with friends in a park. Right now, we need lots of pictures of the same place to train computers to get really good at it. But what if we could use maps instead? That way, we wouldn’t have to take so many photos and computers would be super smart! The new method, called marepo, does just that. It uses a special map to help computers figure out where they should point their camera. This makes it really fast and accurate, beating the old ways by far! |
Keywords
» Artificial intelligence » Regression
