Food Portion Estimation via 3D Object Scaling
This addresses the problem of dietary monitoring for health applications, but it is incremental as it builds on existing image-based methods with a novel 3D approach.
The paper tackles the challenge of accurately estimating food portion sizes from 2D images by proposing a framework that uses 3D food models and physical references to estimate volume and energy, achieving an average error of 31.10 kCal (17.67%) on a new dataset.
Image-based methods to analyze food images have alleviated the user burden and biases associated with traditional methods. However, accurate portion estimation remains a major challenge due to the loss of 3D information in the 2D representation of foods captured by smartphone cameras or wearable devices. In this paper, we propose a new framework to estimate both food volume and energy from 2D images by leveraging the power of 3D food models and physical reference in the eating scene. Our method estimates the pose of the camera and the food object in the input image and recreates the eating occasion by rendering an image of a 3D model of the food with the estimated poses. We also introduce a new dataset, SimpleFood45, which contains 2D images of 45 food items and associated annotations including food volume, weight, and energy. Our method achieves an average error of 31.10 kCal (17.67%) on this dataset, outperforming existing portion estimation methods. The dataset can be accessed at: https://lorenz.ecn.purdue.edu/~gvinod/simplefood45/ and the code can be accessed at: https://gitlab.com/viper-purdue/monocular-food-volume-3d