Visual Vibrometry: Estimating Material Properties from Small Motion in Video
IEEE Conf. on Computer Vision and Pattern Recognition
Abe Davis, Katherine L. Bouman, Justin G. Chen, Michael Rubinstein, Fredo Durand,
William T. Freeman
The estimation of material properties is important for scene understanding, with
many applications in vision, robotics, and structural engineering. This paper
connects fundamentals of vibration mechanics with computer vision techniques in
order to infer material properties from small, often imperceptible motion in video.
Objects tend to vibrate in a set of preferred modes. The shapes and frequencies of
these modes depend on the structure and material properties of an object. Focusing
on the case where geometry is known or fixed, we show how information about an
object’s modes of vibration can be extracted from video and used to make inferences
about that object’s material properties. We demonstrate our approach by estimating
material properties for a variety of rods and fabrics by passively observing their
motion in high-speed and regular frame-rate video.