Summary of A Deep Learning Framework For Three Dimensional Shape Reconstruction From Phaseless Acoustic Scattering Far-field Data, by Doga Dikbayir et al.
A Deep Learning Framework for Three Dimensional Shape Reconstruction from Phaseless Acoustic Scattering Far-field Data
by Doga Dikbayir, Abdel Alsnayyan, Vishnu Naresh Boddeti, Balasubramaniam Shanker, Hasan Metin Aktulga
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The developed deep learning framework uses limited information with single incident wave, single frequency, and phase-less far-field data to reconstruct shapes. A compact probabilistic shape latent space is learned by a 3D variational auto-encoder, while a convolutional neural network maps acoustic scattering information to this shape representation. The proposed method is evaluated on synthetic particle datasets like ShapeNet, achieving accurate reconstructions for complex scatterer shapes like airplanes and automobiles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about solving an important problem in fields like medical imaging and sonar using data. They create a new way to reconstruct shapes by learning from limited information. They use two types of networks: one that learns the shape space and another that maps sound waves to this space. The results show they can accurately recreate complex shapes, even with varying data. |
Keywords
» Artificial intelligence » Deep learning » Encoder » Latent space » Neural network
