Summary of Umono: Physical Model Informed Hybrid Cnn-transformer Framework For Underwater Monocular Depth Estimation, by Jian Wang et al.
UMono: Physical Model Informed Hybrid CNN-Transformer Framework for Underwater Monocular Depth Estimation
by Jian Wang, Jing Wang, Shenghui Rong, Bo He
First submitted to arxiv on: 25 Jul 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 proposes an end-to-end learning framework called UMono for estimating depth from a single underwater image. This task is crucial for tasks like 3D reconstruction of underwater scenes. However, the existing methods fail to consider the unique characteristics of underwater environments, leading to inadequate estimation results and limited generalization performance. To overcome these challenges, the proposed method incorporates underwater image formation model characteristics into network architecture and effectively utilizes both local and global features of underwater images. Experimental results demonstrate that UMono outperforms existing methods in both quantitative and qualitative analyses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to figure out how deep you are under the ocean just by looking at a single picture taken from above the water. This is what researchers call “underwater monocular depth estimation.” They want to make sure this process is accurate, so they developed a new way to do it called UMono. Unlike other methods that don’t take into account how light and water affect the image, UMono uses special techniques to extract important details from the picture and combine them to get an accurate measurement of depth. The results show that UMono works better than previous methods. |
Keywords
» Artificial intelligence » Depth estimation » Generalization
