# How Much You Ate? Food Portion Estimation on Spoons

arXiv: https://arxiv.org/abs/2405.08717 · HTML: https://mlsanity.com/paper/2405.08717/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 30 references that are in the map.
- First posted on arXiv 12 May 2024.

## Builds on (most cited first, 9 of 9)

- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [An End-to-End Food Image Analysis System](https://mlsanity.com/paper/2102.00645/) (2021)
- [An Integrated System for Mobile Image-Based Dietary Assessment](https://mlsanity.com/paper/2110.01754/) (2021)
- [An End-to-end Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Image](https://mlsanity.com/paper/2308.01810/) (2023)
- [A review on vision-based analysis for automatic dietary assessment](https://mlsanity.com/paper/2108.02947/) (2021)
- [NutritionVerse-3D: A 3D Food Model Dataset for Nutritional Intake Estimation](https://mlsanity.com/paper/2304.05619/) (2023)
- [Towards the Creation of a Nutrition and Food Group Based Image Database](https://mlsanity.com/paper/2206.02086/) (2022)

## Built on by (most cited first, 1 of 1)

- [Food Portion Estimation: From Pixels to Calories](https://mlsanity.com/paper/2602.05078/) (2026)

## Abstract (arXiv)

Monitoring dietary intake is a crucial aspect of promoting healthy living. In recent years, advances in computer vision technology have facilitated dietary intake monitoring through the use of images and depth cameras. However, the current state-of-the-art image-based food portion estimation algorithms assume that users take images of their meals one or two times, which can be inconvenient and fail to capture food items that are not visible from a top-down perspective, such as ingredients submerged in a stew. To address these limitations, we introduce an innovative solution that utilizes stationary user-facing cameras to track food items on utensils, not requiring any change of camera perspective after installation. The shallow depth of utensils provides a more favorable angle for capturing food items, and tracking them on the utensil's surface offers a significantly more accurate estimation of dietary intake without the need for post-meal image capture. The system is reliable for estimation of nutritional content of liquid-solid heterogeneous mixtures such as soups and stews. Through a series of experiments, we demonstrate the exceptional potential of our method as a non-invasive, user-friendly, and highly accurate dietary intake monitoring tool.