ArticleNutrients2018
Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map.
Article in Nutrients, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-based digital image dietary assessment methods compared to humans and ground truth: a systematic review.Annals of medicine · 2023Pooled it
- Recognition of eating episodes via commercial smartwatch sensors analysis.PLOS digital health · 2026Article
- Article
- Enhancing dietary management and nutritional analysis through deep learning: a multi-model approach for food classification and volume estimation.Scientific reports · 2026Article
- Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review.Journal of medical Internet research · 2024Article
- Dining Bowl Modeling and Optimization for Single-Image-Based Dietary Assessment.Sensors (Basel, Switzerland) · 2024Article
- Automated Artificial Intelligence-Based Thai Food Dietary Assessment System: Development and Validation.Current developments in nutrition · 2024Article
- Amount Estimation Method for Food Intake Based on Color and Depth Images through Deep Learning.Sensors (Basel, Switzerland) · 2024Article
- Estimating Amount of Food in a Circular Dining Bowl from a Single Image.MADiMa '23 : proceedings of the 8th International Workshop on Multimedia Assisted Dietary Management · 2023Article
- Continuity, Considerations, and Future Directions for the Healthy Eating Index-Toddlers-2020.Journal of the Academy of Nutrition and Dietetics · 2023Article
- Multimedia Data-Based Mobile Applications for Dietary Assessment.Journal of diabetes science and technology · 2023Review
- Learning metric volume estimation of fruits and vegetables from short monocular video sequences.Heliyon · 2023Article
- Improved Wearable Devices for Dietary Assessment Using a New Camera System.Sensors (Basel, Switzerland) · 2022Article
- Dietary Nutritional Information Autonomous Perception Method Based on Machine Vision in Smart Homes.Entropy (Basel, Switzerland) · 2022Article
- Review
- Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes.Scientific reports · 2022Article
- A Cross-Sectional Reproducibility Study of a Standard Camera Sensor Using Artificial Intelligence to Assess Food Items: The FoodIntech Project.Nutrients · 2022Article
- Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System.Electronics · 2021Article
- An automatic electronic instrument for accurate measurements of food volume and density.Public health nutrition · 2021Article
- Artificial Intelligence in Nutrients Science Research: A Review.Nutrients · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
An objective dietary assessment system can help users to understand their dietary behavior and enable targeted interventions to address underlying health problems. To accurately quantify dietary intake, measurement of the portion size or food volume is required. For volume estimation, previous research studies mostly focused on using model-based or stereo-based approaches which rely on manual intervention or require users to capture multiple frames from different viewing angles which can be tedious. In this paper, a view synthesis approach based on deep learning is proposed to reconstruct 3D point clouds of food items and estimate the volume from a single depth image. A distinct neural network is designed to use a depth image from one viewing angle to predict another depth image captured from the corresponding opposite viewing angle. The whole 3D point cloud map is then reconstructed by fusing the initial data points with the synthesized points of the object items through the proposed point cloud completion and Iterative Closest Point (ICP) algorithms. Furthermore, a database with depth images of food object items captured from different viewing angles is constructed with image rendering and used to validate the proposed neural network. The methodology is then evaluated by comparing the volume estimated by the synthesized 3D point cloud with the ground truth volume of the object items.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.