Name
Title | ||
|---|---|---|
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 nondestructive 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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.01575 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Computational photography | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Berthy Feng | 1 | 0 | 0.34 |
| Alexander C. Ogren | 2 | 0 | 0.34 |
| C. Daraio | 3 | 59 | 4.38 |
| Katherine L. Bouman | 4 | 119 | 7.97 |