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A data-driven framework for fast three dimensional shape reconstruction from phaseless acoustic scattering data
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AI
DOI：10.1088/1361-6420/ae537e.png)
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Abstract
En 中文
The acoustic inverse scattering problem is of critical importance in a number of fields, including medical imaging, sonar, and non-destructive evaluation. The problem of interest can vary from the detection of the shape to the properties of an obstacle. The challenge is that this problem is severely ill-posed and highly nonlinear. Significant efforts have been expended over the years to develop solutions to this problem. However, existing fast data-driven methods primarily focus on the two-dimensional scattering case. This paper explores the potential of using machine learning to accelerate the solution to the three-dimensional (3D) version of the problem. To this end, we develop inverse scattering shape reconstruction network (ISSRNet), a deep learning framework for 3D shape reconstruction using phaseless far-field data. The framework is implemented by (a) using a compact probabilistic shape latent space learned by a 3D variational auto-encoder, and (b) a convolutional neural network trained to extract far-field features due to multiple incident waves and map the acoustic scattering information to this shape representation. We demonstrate ISSRNet's 3D shape reconstruction capabilities on random rock-like particles, and airplane objects from the popular ShapeNet data set. We also evaluate the framework's performance when trained on lower-resolution scattering data and when receiver locations include uncertainty. Our experiments show that the proposed framework is able to capture both global and local details, differentiate between different types of shapes and performs several orders of magnitude faster than its numerical iterative counterparts.
Keywords:
3D shape recognition
inverse scattering problems
machine learning
Journal
I
IF:
2.1
Papers:
97
Citations:
8.4K