# Visual Vibration Tomography: Estimating Interior Material Properties from Monocular Video

arXiv: https://arxiv.org/abs/2104.02735 · HTML: https://mlsanity.com/paper/2104.02735/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 70 references that are in the map.
- First posted on arXiv 6 Apr 2021.

## Builds on (most cited first, 2 of 2)

- [Material Recognition in the Wild with the Materials in Context Database](https://mlsanity.com/paper/1412.0623/) (2014)
- [Vibration-Based Damage Detection in Wind Turbine Blades using Phase-Based Motion Estimation and Motion Magnification](https://mlsanity.com/paper/1804.00558/) (2018)

## Built on by (most cited first, 5 of 5)

- [Neural Implicit Representations for Physical Parameter Inference from a Single Video](https://mlsanity.com/paper/2204.14030/) (2022)
- [Event2Audio: Event-Based Optical Vibration Sensing](https://mlsanity.com/paper/2507.03273/) (2025)
- [MaNGO - Adaptable Graph Network Simulators via Meta-Learning](https://mlsanity.com/paper/2510.05874/) (2025)
- [Visual Surface Wave Elastography: Revealing Subsurface Physical Properties via Visible Surface Waves](https://mlsanity.com/paper/2507.09207/) (2025)
- [Learning to See Inside Opaque Liquid Containers using Speckle Vibrometry](https://mlsanity.com/paper/2507.20757/) (2025)

## Abstract (arXiv)

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.