Summary of Scalable Cloud-native Pipeline For Efficient 3d Model Reconstruction From Monocular Smartphone Images, by Potito Aghilar et al.
Scalable Cloud-Native Pipeline for Efficient 3D Model Reconstruction from Monocular Smartphone Images
by Potito Aghilar, Vito Walter Anelli, Michelantonio Trizio, Tommaso Di Noia
First submitted to arxiv on: 28 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 proposed cloud-native pipeline automatically reconstructs 3D models from monocular 2D images captured using a smartphone camera. Leveraging NVIDIA Research Labs’ machine learning models and Google’s ARCore framework, the solution produces reusable 3D models with embedded materials and textures. The pipeline is designed to be efficient, easily adoptable, and meets Industry 4.0 standards for creating Digital Twin models. This enables accelerated training of personnel and enhances expertise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a smartphone that can create 3D models just like a computer program! Researchers have developed a way to use artificial intelligence to make this happen. They created a special process called a pipeline that takes pictures taken with a smartphone camera and turns them into 3D models. These models are not only realistic but also include textures and materials, making them useful for industries like manufacturing or entertainment. |
Keywords
» Artificial intelligence » Machine learning
