# Two-view 3D Reconstruction for Food Volume Estimation

arXiv: https://arxiv.org/abs/1701.03330 · HTML: https://mlsanity.com/paper/1701.03330/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2017.

## Built on by (most cited first, 19 of 19)

- [Food Portion Estimation via 3D Object Scaling](https://mlsanity.com/paper/2404.12257/) (2024)
- [DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion](https://mlsanity.com/paper/2310.11702/) (2023)
- [A review on vision-based analysis for automatic dietary assessment](https://mlsanity.com/paper/2108.02947/) (2021)
- [MFP3D: Monocular Food Portion Estimation Leveraging 3D Point Clouds](https://mlsanity.com/paper/2411.10492/) (2024)
- [A Comprehensive Survey of Image-Based Food Recognition and Volume Estimation Methods for Dietary Assessment](https://mlsanity.com/paper/2106.11776/) (2021)
- [VolETA: One- and Few-shot Food Volume Estimation](https://mlsanity.com/paper/2407.01717/) (2024)
- [Food Portion Estimation: From Pixels to Calories](https://mlsanity.com/paper/2602.05078/) (2026)
- [Material Measurement Units for a Circular Economy: Foundations through a Review](https://mlsanity.com/paper/2103.01997/) (2021)
- [Enhancing Food Intake Tracking in Long-Term Care with Automated Food Imaging and Nutrient Intake Tracking (AFINI-T) Technology](https://mlsanity.com/paper/2112.04608/) (2021)
- [VolE: A Point-cloud Framework for Food 3D Reconstruction and Volume Estimation](https://mlsanity.com/paper/2505.10205/) (2025)
- [Fine-Tuned Vision Transformers Capture Complex Wheat Spike Morphology for Volume Estimation from RGB Images](https://mlsanity.com/paper/2506.18060/) (2025)
- [Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perception](https://mlsanity.com/paper/2604.09886/) (2026)
- [An Intelligent Passive Food Intake Assessment System with Egocentric Cameras](https://mlsanity.com/paper/2105.03142/) (2021)
- [ANTHROPOS-V: benchmarking the novel task of Crowd Volume Estimation](https://mlsanity.com/paper/2501.01877/) (2025)
- [PortionNet: Distilling 3D Geometric Knowledge for Food Nutrition Estimation](https://mlsanity.com/paper/2512.22304/) (2025)
- [DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images](https://mlsanity.com/paper/2604.06352/) (2026)
- [Lightweight Neural Framework for Robust 3D Volume and Surface Estimation from Multi-View Images](https://mlsanity.com/paper/2606.23653/) (2026)
- [Doppio: A Dataset for Contactless Weight Estimation of Falling Particles](https://mlsanity.com/paper/2609.02528/) (2026)
- [Reference-Free Food Volume Estimation from Egocentric Stereo Video](https://mlsanity.com/paper/2610.12735/) (2026)

## Abstract (arXiv)

The increasing prevalence of diet-related chronic diseases coupled with the ineffectiveness of traditional diet management methods have resulted in a need for novel tools to accurately and automatically assess meals. Recently, computer vision based systems that use meal images to assess their content have been proposed. Food portion estimation is the most difficult task for individuals assessing their meals and it is also the least studied area. The present paper proposes a three-stage system to calculate portion sizes using two images of a dish acquired by mobile devices. The first stage consists in understanding the configuration of the different views, after which a dense 3D model is built from the two images; finally, this 3D model serves to extract the volume of the different items. The system was extensively tested on 77 real dishes of known volume, and achieved an average error of less than 10% in 5.5 seconds per dish. The proposed pipeline is computationally tractable and requires no user input, making it a viable option for fully automated dietary assessment.