Summary of Estimating Body Volume and Height Using 3d Data, by Vivek Ganesh Sonar et al.
Estimating Body Volume and Height Using 3D Data
by Vivek Ganesh Sonar, Muhammad Tanveer Jan, Mike Wells, Abhijit Pandya, Gabriela Engstrom, Richard Shih, Borko Furht
First submitted to arxiv on: 18 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 The paper presents a non-invasive method for estimating body weight using 3D imaging technology. A RealSense D415 camera captures high-resolution depth maps of the patient, which are then processed to generate 3D models. The Convex Hull Algorithm is applied to calculate total body volume, and height is derived from the 3D model by identifying key points on the body. This combined approach provides an accurate estimate of body weight, improving patient safety and treatment outcomes in emergency settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors give patients the right medication in emergencies. Right now, it’s hard to measure people’s weights directly when they’re in a hurry. So, researchers used special cameras to take pictures of people from different angles. They then used computer algorithms to create 3D models of these people. By measuring the size and shape of these models, doctors can estimate how much someone weighs. This is important because it helps ensure that patients get the right treatment in emergency situations. |
