Pop’n Food: 3D Food Model Estimation System from a Single Image

Abstract

Dietary calorie management has been an important topic in recent years, and various methods and applications on image-based food calorie estimation have been published in the multimedia community. Most of the existing methods of estimating food calorie amounts use 2D-based image recognition. However, since actual food is a 3D object, there is a limit to the accuracy of calorie estimation using 2D-based methods. Therefore, in our previous work, we proposed a method to reconstruct the 3D shape of the dish (food and plate) and a plate (without foods) from a single 2D image and estimate a more accurate food volume. Such researches on 3D reconstruction have been active recently, and it is necessary to qualitatively evaluate what kind of 3D shape has been reconstructed. However, checking a large number of 3D models reconstructed from a large number of images requires many steps and is tedious. Against this background, this demo paper introduces an application named "Pop’n Food" which has the following two functions: (1) A web application for visualizing a large number of images to check the learning results and the 3D model generated from them. (2) A web application that selects an image from a browser and generates and visualizes a 3D model in real-time. This demo system is based on our previous work named Hungry Networks. Demo video: https://youtu.be/YyIu8bL65EE