Summary of Medea: Multi-view Efficient Depth Adjustment, by Mikhail Artemyev et al.
MEDeA: Multi-view Efficient Depth Adjustment
by Mikhail Artemyev, Anna Vorontsova, Anna Sokolova, Alexander Limonov
First submitted to arxiv on: 17 Jun 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 paper presents an efficient multi-view test-time depth adjustment method called MEDeA, which is faster than existing approaches by an order of magnitude. The method predicts initial depth maps, adjusts them by optimizing local scaling coefficients, and outputs temporally-consistent depth maps. Unlike other test-time methods that require normals, optical flow, or semantics estimation, MEDeA uses a depth estimation network solely to produce high-quality predictions. The paper sets new state-of-the-art results on TUM RGB-D, 7Scenes, and ScanNet benchmarks, and successfully handles smartphone-captured data from ARKitScenes dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MEDeA is a new method for predicting depths in real-world scenarios. It’s really fast, unlike other methods that take hours to process one scene. The method takes RGB images with camera information, predicts the initial depth, adjusts it to make sure it’s consistent over time, and gives you high-quality predictions. |
Keywords
» Artificial intelligence » Depth estimation » Optical flow » Semantics
