Food Portion Estimation: From Pixels to Calories

2026-02-04

AI Summary

This study addresses the challenge of accurately estimating the three-dimensional volume of food from a single 2D image for caloric assessment. To this end, it systematically investigates and integrates multiple image-to-portion mapping strategies, including deep learning, monocular image analysis, depth map fusion, multi-view geometry, and template matching. Without relying on complex hardware, the proposed approach significantly enhances the accuracy of food volume and calorie estimation by comprehensively comparing and optimizing these diverse methods. The resulting framework offers a practical and high-precision solution for everyday dietary monitoring, thereby advancing the field of automated nutritional intake assessment based solely on ordinary images.

Technology Category

• Computer Vision: Image and Video Retrieval
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: State Estimation

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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.

Problem

• food portion estimation
• image-based dietary assessment
• 3D food size estimation
• monocular image
• calorie estimation

Innovation

• food portion estimation
• monocular depth estimation
• deep learning
• dietary assessment
• 3D food reconstruction