# See the Glass Half Full: Reasoning about Liquid Containers, their Volume and Content

## TL;DR

This paper introduces a novel computer vision approach for estimating the volume and contents of liquid containers from a single RGB image, along with a new dataset called COQE for training and evaluation.

## Contribution

It presents methods for volume estimation, content approximation, and behavior prediction of liquids in containers, and introduces the COQE dataset with extensive annotations.

## Key findings

- Effective volume and content estimation from RGB images.
- Successful prediction of liquid behavior when tilting containers.
- Creation of a comprehensive dataset for liquid container analysis.

## Abstract

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.