Summary of Rethinking Rgb-d Fusion For Semantic Segmentation in Surgical Datasets, by Muhammad Abdullah Jamal et al.
Rethinking RGB-D Fusion for Semantic Segmentation in Surgical Datasets
by Muhammad Abdullah Jamal, Omid Mohareri
First submitted to arxiv on: 29 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 A novel multi-modal training framework called SurgDepth is proposed for surgical scene understanding, achieving state-of-the-art results on publicly available datasets. The framework uses Vision Transformers (ViTs) to encode both RGB and depth information through a simple fusion mechanism. This approach outperforms previous methods by at least 4%, using a shallow and compute-efficient decoder consisting of ConvNeXt blocks. The SurgDepth model achieves a new state-of-the-art IoU of 0.86 on the EndoVis2022 SAR-RARP50 challenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SurgDepth is a new way to help computers understand surgical scenes. It’s like training a computer to look at and analyze pictures taken during surgeries. This can help make surgeries better, safer, and more efficient. The SurgDepth model uses two kinds of information: what the scene looks like (RGB) and how far away things are (depth). By combining these two types of information, SurgDepth is able to understand surgical scenes in a way that’s even better than previous methods. |
Keywords
» Artificial intelligence » Decoder » Multi modal » Scene understanding
