# Perceiving and Reasoning About Liquids Using Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1703.01564 · HTML: https://mlsanity.com/paper/1703.01564/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2017.

## Built on by (most cited first, 10 of 10)

- [3D Neural Scene Representations for Visuomotor Control](https://mlsanity.com/paper/2107.04004/) (2021)
- [Learning Multi-Object Dynamics with Compositional Neural Radiance Fields](https://mlsanity.com/paper/2202.11855/) (2022)
- [Seeing Glass: Joint Point Cloud and Depth Completion for Transparent Objects](https://mlsanity.com/paper/2110.00087/) (2021)
- [Physics perception in sloshing scenes with guaranteed thermodynamic consistency](https://mlsanity.com/paper/2106.13301/) (2021)
- [A Thermodynamics-informed Active Learning Approach to Perception and Reasoning about Fluids](https://mlsanity.com/paper/2203.05775/) (2022)
- [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)
- [HemoSet: The First Blood Segmentation Dataset for Automation of Hemostasis Management](https://mlsanity.com/paper/2403.16286/) (2024)
- [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)
- [Learning to Segment Liquids in Real-world Images](https://mlsanity.com/paper/2601.00940/) (2026)

## Abstract (arXiv)

Liquids are an important part of many common manipulation tasks in human environments. If we wish to have robots that can accomplish these types of tasks, they must be able to interact with liquids in an intelligent manner. In this paper, we investigate ways for robots to perceive and reason about liquids. That is, a robot asks the questions What in the visual data stream is liquid? and How can I use that to infer all the potential places where liquid might be? We collected two datasets to evaluate these questions, one using a realistic liquid simulator and another on our robot. We used fully convolutional neural networks to learn to detect and track liquids across pouring sequences. Our results show that these networks are able to perceive and reason about liquids, and that integrating temporal information is important to performing such tasks well.