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

arXiv: https://arxiv.org/abs/1701.02718 · HTML: https://mlsanity.com/paper/1701.02718/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jan 2017.

## Built on by (most cited first, 11 of 11)

- [Open-Vocabulary Affordance Detection in 3D Point Clouds](https://mlsanity.com/paper/2303.02401/) (2023)
- [From an Image to a Scene: Learning to Imagine the World from a Million 360 Videos](https://mlsanity.com/paper/2412.07770/) (2024)
- [The CORSMAL benchmark for the prediction of the properties of containers](https://mlsanity.com/paper/2107.12719/) (2021)
- [Improving filling level classification with adversarial training](https://mlsanity.com/paper/2102.04057/) (2021)
- [The Sound of Water: Inferring Physical Properties from Pouring Liquids](https://mlsanity.com/paper/2411.11222/) (2024)
- [Image Based Reconstruction of Liquids from 2D Surface Detections](https://mlsanity.com/paper/2111.11491/) (2021)
- [DAViD: Modeling Dynamic Affordance of 3D Objects Using Pre-trained Video Diffusion Models](https://mlsanity.com/paper/2501.08333/) (2025)
- [Predicting 3D shapes, masks, and properties of materials, liquids, and objects inside transparent containers, using the TransProteus CGI dataset](https://mlsanity.com/paper/2109.07577/) (2021)
- [Audio-Visual Object Classification for Human-Robot Collaboration](https://mlsanity.com/paper/2203.01977/) (2022)
- [Vision-based robot manipulation of transparent liquid containers in a laboratory setting](https://mlsanity.com/paper/2404.16529/) (2024)
- [Data augmentation with mixtures of max-entropy transformations for filling-level classification](https://mlsanity.com/paper/2203.04027/) (2022)

## Abstract (arXiv)

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.