Summary of Adaptive Stereo Depth Estimation with Multi-spectral Images Across All Lighting Conditions, by Zihan Qin et al.
Adaptive Stereo Depth Estimation with Multi-Spectral Images Across All Lighting Conditions
by Zihan Qin, Jialei Xu, Wenbo Zhao, Junjun Jiang, Xianming Liu
First submitted to arxiv on: 6 Nov 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 A novel framework is proposed to improve multi-spectral depth estimation under adverse conditions by incorporating stereo depth estimation and enforcing accurate geometric constraints. The framework treats visible light and thermal images as a stereo pair and uses a Cross-modal Feature Matching (CFM) Module to construct a cost volume for pixel-level matching. To mitigate the effects of poor lighting on stereo matching, Degradation Masking is introduced, which leverages robust monocular thermal depth estimation in degraded regions. The method achieves state-of-the-art performance on the Multi-Spectral Stereo (MS2) dataset and produces high-quality depth maps under varying lighting conditions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to improve how computers estimate distance from multiple types of images is developed. This method combines two kinds of images, one that shows what’s visible and one that shows heat, like a thermal image. It uses special computer vision techniques to match up the features in these two images and get accurate measurements of depth. The system also has a way to deal with situations where the lighting is poor, which can make it hard for computers to estimate distance accurately. This method works really well on a special dataset that tests its ability to work in different lighting conditions. |
Keywords
» Artificial intelligence » Depth estimation
