Summary of Geometric Constraints in Deep Learning Frameworks: a Survey, by Vibhas K Vats et al.
Geometric Constraints in Deep Learning Frameworks: A Survey
by Vibhas K Vats, David J Crandall
First submitted to arxiv on: 19 Mar 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 explores the emerging technique of stereophotogrammetry, which has its origins in the 1800s. It presents a survey of the overlap between geometric-based and deep learning-based frameworks for scene understanding. The authors compare and contrast geometry-enforcing constraints integrated into deep learning frameworks for depth estimation or related problems. They also introduce a new taxonomy for prevalent geometry-enforcing constraints used in modern deep learning frameworks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how we understand scenes using photographs. It compares two ways of doing this: one that uses math to figure out the scene, and another that uses computers to learn from examples. The authors show how these two approaches can be combined to get better results. They also suggest new directions for future research. |
Keywords
* Artificial intelligence * Deep learning * Depth estimation * Scene understanding
