Summary of Chosen: Contrastive Hypothesis Selection For Multi-view Depth Refinement, by Di Qiu et al.
CHOSEN: Contrastive Hypothesis Selection for Multi-View Depth Refinement
by Di Qiu, Yinda Zhang, Thabo Beeler, Vladimir Tankovich, Christian Häne, Sean Fanello, Christoph Rhemann, Sergio Orts Escolano
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We propose CHOSEN, a novel multi-view depth refinement framework that can be seamlessly integrated into existing multi-view stereo pipelines. This flexible approach adapts to different capture systems, including camera relative positioning and lenses, by iteratively refining initial depth estimates through hypothesis selection and adaptation to metric or intrinsic scales. The key innovation lies in the application of contrastive learning within a carefully designed solution space, enabling effective distinction between positive and negative hypotheses. By incorporating CHOSEN into a simple baseline multi-view stereo pipeline, we achieve impressive gains in depth and normal accuracy compared to state-of-the-art deep learning-based approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a tool that can take many different photos from different angles and combine them to create a super accurate 3D picture of the world. That’s what our new method, called CHOSEN, does. It takes an initial guess at what the depth (or distance) is between objects in a scene and then refines it by selecting the best guesses and adapting to different types of cameras or lenses used to take the photos. Our approach uses clever math tricks to figure out which guesses are most likely to be correct. When we tested our method with real data, we found that it works much better than other computer vision techniques at creating accurate 3D pictures. |
Keywords
* Artificial intelligence * Deep learning
