Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video

Abstract

An object's interior material properties, while invisible to the human eye, determine motion observed on its surface. We propose an approach that estimates heterogeneous material properties of an object from a monocular video of its surface vibrations. Specifically, we show how to estimate Young's modulus and density throughout a 3D object with known geometry. Knowledge of how these values change across the object is useful for simulating its motion and characterizing any defects. Traditional non-destructive testing approaches, which often require expensive instruments, generally estimate only homogenized material properties or simply identify the presence of defects. In contrast, our approach leverages monocular video to (1) identify image-space modes from an object's sub-pixel motion, and (2) directly infer spatially-varying Young's modulus and density values from the observed modes. We demonstrate our approach on both simulated and real videos.

Cite

Text

Feng et al. "Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video." Conference on Computer Vision and Pattern Recognition, 2022. doi:10.1109/CVPR52688.2022.01575

Markdown

[Feng et al. "Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video." Conference on Computer Vision and Pattern Recognition, 2022.](https://mlanthology.org/cvpr/2022/feng2022cvpr-visual/) doi:10.1109/CVPR52688.2022.01575

BibTeX

@inproceedings{feng2022cvpr-visual,
  title     = {{Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video}},
  author    = {Feng, Berthy T. and Ogren, Alexander C. and Daraio, Chiara and Bouman, Katherine L.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2022},
  pages     = {16231-16240},
  doi       = {10.1109/CVPR52688.2022.01575},
  url       = {https://mlanthology.org/cvpr/2022/feng2022cvpr-visual/}
}