Summary of Advancing Depth Anything Model For Unsupervised Monocular Depth Estimation in Endoscopy, by Bojian Li et al.
Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy
by Bojian Li, Bo Liu, Xinning Yao, Jinghua Yue, Fugen Zhou
First submitted to arxiv on: 12 Sep 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 approach to depth estimation is proposed, leveraging foundation models and a fine-tuning strategy for minimally invasive endoscopic surgeries. The Depth Anything Model is adapted using an intrinsic-based unsupervised monocular depth estimation framework, incorporating low-rank adaptation and residual blocks to enhance performance. Experimental results on the SCARED and Hamlyn datasets demonstrate state-of-the-art performance while minimizing trainable parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has developed a new way to help surgeons during minimally invasive operations. They used special computer models to estimate depth and improve spatial awareness. This is important because it can make surgeries safer and more precise. The scientists combined two different methods: one that looks at the whole image, and another that focuses on specific details. They tested their approach with real-world data and found that it worked really well. |
Keywords
» Artificial intelligence » Depth estimation » Fine tuning » Low rank adaptation » Unsupervised
