  Humans have rich understanding of liquid containers and their contents; for
example, we can effortlessly pour water from a pitcher to a cup. Doing so
requires estimating the volume of the cup, approximating the amount of water in
the pitcher, and predicting the behavior of water when we tilt the pitcher.
Very little attention in computer vision has been made to liquids and their
containers. In this paper, we study liquid containers and their contents, and
propose methods to estimate the volume of containers, approximate the amount of
liquid in them, and perform comparative volume estimations all from a single
RGB image. Furthermore, we show the results of the proposed model for
predicting the behavior of liquids inside containers when one tilts the
containers. We also introduce a new dataset of Containers Of liQuid contEnt
(COQE) that contains more than 5,000 images of 10,000 liquid containers in
context labelled with volume, amount of content, bounding box annotation, and
corresponding similar 3D CAD models.
