# Food Portion Estimation: From Pixels to Calories

arXiv: https://arxiv.org/abs/2602.05078 · HTML: https://mlsanity.com/paper/2602.05078/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 4 Feb 2026.

## Builds on (most cited first, 12 of 12)

- [Nutrition5k: Towards Automatic Nutritional Understanding of Generic Food](https://mlsanity.com/paper/2103.03375/) (2021)
- [Two-view 3D Reconstruction for Food Volume Estimation](https://mlsanity.com/paper/1701.03330/) (2017)
- [Single-View Food Portion Estimation: Learning Image-to-Energy Mappings Using Generative Adversarial Networks](https://mlsanity.com/paper/1802.09670/) (2018)
- [An End-to-end Food Portion Estimation Framework Based on Shape Reconstruction from Monocular Image](https://mlsanity.com/paper/2308.01810/) (2023)
- [Food Portion Estimation via 3D Object Scaling](https://mlsanity.com/paper/2404.12257/) (2024)
- [Image Based Food Energy Estimation With Depth Domain Adaptation](https://mlsanity.com/paper/2208.12153/) (2022)
- [DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion](https://mlsanity.com/paper/2310.11702/) (2023)
- [MetaFood CVPR 2024 Challenge on Physically Informed 3D Food Reconstruction: Methods and Results](https://mlsanity.com/paper/2407.09285/) (2024)
- [MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Clouds](https://mlsanity.com/paper/2411.10492/) (2024)
- [Simulating Personal Food Consumption Patterns using a Modified Markov Chain](https://mlsanity.com/paper/2208.06709/) (2022)
- [NutritionVerse-Direct: Exploring Deep Neural Networks for Multitask Nutrition Prediction from Food Images](https://mlsanity.com/paper/2405.07814/) (2024)
- [How Much You Ate? Food Portion Estimation on Spoons](https://mlsanity.com/paper/2405.08717/) (2024)

## Built on by (most cited first, 3 of 3)

- [Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception](https://mlsanity.com/paper/2604.09886/) (2026)
- [DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images](https://mlsanity.com/paper/2604.06352/) (2026)
- [Open-KNEAD: Knowledge-grounded Nutrition Estimation via Agentic Decomposition](https://mlsanity.com/paper/2607.12911/) (2026)

## Abstract (arXiv)

Reliance on images for dietary assessment is an important strategy to accurately and conveniently monitor an individual's health, making it a vital mechanism in the prevention and care of chronic diseases and obesity. However, image-based dietary assessment suffers from estimating the three dimensional size of food from 2D image inputs. Many strategies have been devised to overcome this critical limitation such as the use of auxiliary inputs like depth maps, multi-view inputs, or model-based approaches such as template matching. Deep learning also helps bridge the gap by either using monocular images or combinations of the image and the auxillary inputs to precisely predict the output portion from the image input. In this paper, we explore the different strategies employed for accurate portion estimation.