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Data-driven volumetric reconstruction for optically measured sound field using physics-constrained 3D Gaussian

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Area of Science:

  • Acoustics
  • Computer Vision
  • Signal Processing

Background:

  • Acousto-optic sensing offers high-resolution sound measurement but faces challenges due to line integral data.
  • Existing physical-model-based reconstruction methods are limited by basis function choices, restricting their applicability to diverse sound fields.

Purpose of the Study:

  • To develop a data-driven sound-field reconstruction method capable of handling high-complexity sound fields.
  • To adapt and extend the 3D Gaussian splatting (3DGS) technique for accurate three-dimensional (3D) sound field reconstruction.

Main Methods:

  • Leveraged a 3D Gaussian splatting (3DGS) scheme, a computer vision technique representing scenes with Gaussian kernels.
  • Extended the R2-Gaussian volume reconstruction approach to accommodate arbitrary real numbers for sound field representation.
  • Incorporated a Helmholtz loss function into the optimization process for improved reconstruction accuracy.

Main Results:

  • The proposed 3DGS-based method, R2-Gaussian, demonstrated successful reconstruction of sound fields.
  • Evaluations included 11 simulated and 1 measured sound field, validating the approach's effectiveness across various scenarios.
  • The data-driven nature allows for greater flexibility compared to traditional basis function-dependent models.

Conclusions:

  • The 3DGS-based data-driven approach offers a powerful solution for reconstructing complex 3D sound fields.
  • This method overcomes limitations of traditional techniques, paving the way for more versatile acousto-optic sensing applications.
  • Future work may explore further optimizations and real-world deployment of this advanced sound field reconstruction technique.