Summary of Stereo Risk: a Continuous Modeling Approach to Stereo Matching, by Ce Liu et al.
Stereo Risk: A Continuous Modeling Approach to Stereo Matching
by Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, Yao Yao, Luc Van Gool
First submitted to arxiv on: 3 Jul 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 We present Stereo Risk, a novel deep-learning approach for solving the stereo-matching problem in computer vision. The traditional state-of-the-art methods rely on regressing scene disparity values via discretization of scene depth. However, this often fails to capture the nuanced and continuous nature of scene depth. Stereo Risk instead formulates scene disparity as an optimal solution to a continuous risk minimization problem. We demonstrate that L^1 minimization of the proposed risk function enhances stereo-matching performance for deep networks, particularly for disparities with multi-modal probability distributions. To enable end-to-end network training of the non-differentiable L^1 risk optimization, we employed the implicit function theorem to ensure a fully differentiable network. Our method outperforms state-of-the-art methods across various benchmark datasets, including KITTI 2012, KITTI 2015, ETH3D, SceneFlow, and Middlebury 2014. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve developed a new way to match images together in computer vision. This is called stereo matching. Normally, people use a method called regressing to do this, but it’s not very good at capturing the subtle details of real-world scenes. Our approach is different – we treat the scene depth as a problem that can be solved by minimizing a risk function. We show that this works better than existing methods for some types of images. We tested our method on many famous benchmark datasets and found it was better than other approaches in most cases. |
Keywords
* Artificial intelligence * Deep learning * Multi modal * Optimization * Probability
