Visual Surface Wave Elastography: Revealing Subsurface Physical Properties via Visible Surface Waves

• URL: http://arxiv.org/abs/2507.09207v1
• Date: Sat, 12 Jul 2025 09:00:06 GMT
• Title: Visual Surface Wave Elastography: Revealing Subsurface Physical Properties via Visible Surface Waves
• Authors: Alexander C. Ogren, Berthy T. Feng, Jihoon Ahn, Katherine L. Bouman, Chiara Daraio,
• Abstract summary: We propose a method for inferring the thickness and stiffness of a structure from just a video of waves on its surface.<n>We validate our method on both simulated and real data, in both cases showing strong agreement with ground-truth measurements.
• Score: 46.02957810536667
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Wave propagation on the surface of a material contains information about physical properties beneath its surface. We propose a method for inferring the thickness and stiffness of a structure from just a video of waves on its surface. Our method works by extracting a dispersion relation from the video and then solving a physics-based optimization problem to find the best-fitting thickness and stiffness parameters. We validate our method on both simulated and real data, in both cases showing strong agreement with ground-truth measurements. Our technique provides a proof-of-concept for at-home health monitoring of medically-informative tissue properties, and it is further applicable to fields such as human-computer interaction.

Related papers

• The Sound of Water: Inferring Physical Properties from Pouring Liquids [85.30865788636386]
  We study the connection between audio-visual observations and the underlying physics of pouring liquids.<n>Our objective is to automatically infer physical properties such as the liquid level, the shape and size of the container, the pouring rate and the time to fill.
  arXiv  Detail & Related papers  (2024-11-18T01:19:37Z)
• PhysMamba: State Space Duality Model for Remote Physiological Measurement [18.423806804725032]
  Remote Photoplethysmography (rBFC) enables non-contact physiological signal extraction from facial videos.<n>This work lays a strong foundation for practical applications in non-contact health monitoring, including real-time remote patient care.
  arXiv  Detail & Related papers  (2024-08-02T07:52:28Z)
• Implicit Neural Representation for Physics-driven Actuated Soft Bodies [15.261578025057593]
  This paper utilizes a differentiable, quasi-static, and physics-based simulation layer to optimize for actuation signals parameterized by neural networks. We define a function that enables a continuous mapping from a spatial point in the material space to the actuation value. We extend our implicit model to mandible kinematics for the particular case of facial animation and show that we can reliably reproduce facial expressions captured with high-quality capture systems.
  arXiv  Detail & Related papers  (2024-01-26T13:42:12Z)
• S2P3: Self-Supervised Polarimetric Pose Prediction [55.43547228561919]
  This paper proposes the first self-supervised 6D object pose prediction from multimodal RGB+polarimetric images. The novel training paradigm comprises 1) a physical model to extract geometric information of polarized light, 2) a teacher-student knowledge distillation scheme and 3) a self-supervised loss formulation through differentiable constraints.
  arXiv  Detail & Related papers  (2023-12-02T10:46:40Z)
• DiffSDFSim: Differentiable Rigid-Body Dynamics With Implicit Shapes [9.119424247289857]
  Differentiable physics is a powerful tool in computer and robotics for scene understanding and reasoning about interactions. Existing approaches have frequently been limited to objects with simple shape or shapes that are in advance.
  arXiv  Detail & Related papers  (2021-11-30T11:56:24Z)
• Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video [66.94502090429806]
  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.
  arXiv  Detail & Related papers  (2021-04-06T18:05:27Z)
• Visual Grounding of Learned Physical Models [66.04898704928517]
  Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. We present a neural model that simultaneously reasons about physics and makes future predictions based on visual and dynamics priors. Experiments show that our model can infer the physical properties within a few observations, which allows the model to quickly adapt to unseen scenarios and make accurate predictions into the future.
  arXiv  Detail & Related papers  (2020-04-28T17:06:38Z)
• Cloth in the Wind: A Case Study of Physical Measurement through Simulation [50.31424339972478]
  We propose to measure latent physical properties for cloth in the wind without ever having seen a real example before. Our solution is an iterative refinement procedure with simulation at its core. The correspondence is measured using an embedding function that maps physically similar examples to nearby points.
  arXiv  Detail & Related papers  (2020-03-09T21:32:23Z)
• Shape analysis via inconsistent surface registration [4.367664806447789]
  We develop a framework for shape analysis using inconsistent surface mapping. Our method is capable of solving this problem using inconsistent surface registration based on quasi-conformal theory.
  arXiv  Detail & Related papers  (2020-03-03T06:58:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.