Summary of Hola: Hololens Object Labeling, by Michael Schwimmbeck et al.
HOLa: HoloLens Object Labeling
by Michael Schwimmbeck, Serouj Khajarian, Konstantin Holzapfel, Johannes Schmidt, Stefanie Remmele
First submitted to arxiv on: 6 Dec 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 A new application for medical Augmented Reality (AR) has been developed, focusing on object tracking and segmentation. The Segment Anything Model (SAM) is used as a foundation, allowing for zero-shot segmentation with minimal human participation. The HoloLens-Object-Labeling (HOLa) application uses the SAM-Track algorithm to provide fully automatic single-object annotation for HoloLens 2 devices. This tool eliminates the need for adjustments based on image appearance and can be applied to various AR research fields. Evaluation of HOLa was conducted in open liver surgery and medical phantom experiments, demonstrating increased labeling speed (over 500 times) and comparable Dice scores (0.875-0.982) to human annotators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new tool for medical Augmented Reality helps doctors track objects and identify important features. This tool uses a model called SAM to quickly and accurately label images without needing much human help. The application, called HOLa, works with special devices like HoloLens 2. It’s easy to use and doesn’t require adjusting settings based on the image. The tool was tested in real surgery and lab experiments and showed that it can be much faster than humans while still giving accurate results. |
Keywords
» Artificial intelligence » Object tracking » Sam » Zero shot
