Dietary assessment is essential to maintaining a healthy lifestyle. Automatic
image-based dietary assessment is a growing field of research due to the
increasing prevalence of image capturing devices (e.g. mobile phones). In this
work, we estimate food energy from a single monocular image, a difficult task
due to the limited hard-to-extract amount of energy information present in an
image. To do so, we employ an improved encoder-decoder framework for energy
estimation; the encoder transforms the image into a representation embedded
with food energy information in an easier-to-extract format, which the decoder
then extracts the energy information from. To implement our method, we compile
a high-quality food image dataset verified by registered dietitians containing
eating scene images, food-item segmentation masks, and ground truth calorie
values. Our method improves upon previous caloric estimation methods by over
10\% and 30 kCal in terms of MAPE and MAE respectively.

通过改进的编码器 - 解码器框架，我们能够从单个单目图像中估算食物的能量信息，通过在易于提取的格式中嵌入图像并由解码器提取能量信息，实现超过 10% 的改进，MAPE 上高于以往的能量估算方法超过 30 kCal 的改善。

一种改进的食物能量估计的编码器 - 解码器框架

An Improved Encoder-Decoder Framework for Food EnergyEstimation

Dietary assessment is a key contributor to monitoring health status. Existing
self-report methods are tedious and time-consuming with substantial biases and
errors. Image-based food portion estimation aims to estimate food energy values
directly from food images, showing great potential for automated dietary
assessment solutions. Existing image-based methods either use a single-view
image or incorporate multi-view images and depth information to estimate the
food energy, which either has limited performance or creates user burdens. In
this paper, we propose an end-to-end deep learning framework for food energy
estimation from a monocular image through 3D shape reconstruction. We leverage
a generative model to reconstruct the voxel representation of the food object
from the input image to recover the missing 3D information. Our method is
evaluated on a publicly available food image dataset Nutrition5k, resulting a
Mean Absolute Error (MAE) of 40.05 kCal and Mean Absolute Percentage Error
(MAPE) of 11.47% for food energy estimation. Our method uses RGB image as the
only input at the inference stage and achieves competitive results compared to
the existing method requiring both RGB and depth information.

通过使用单目图像进行三维形状重建，我们提出了一种端到端深度学习框架，用于从食物图像中估计食物能量，其在公开的食品图像数据集 Nutrition5k 上评估结果显示，我们的方法具有竞争力的性能，只需使用 RGB 图像作为推理阶段的唯一输入。

基于单眼图像的形状重建的端到端食物分量估计框架

An End-to-end Food Portion Estimation Framework Based on Shape  Reconstruction from Monocular Image

Assessment of dietary intake has primarily relied on self-report instruments,
which are prone to measurement errors. Dietary assessment methods have
increasingly incorporated technological advances particularly mobile, image
based approaches to address some of these limitations and further automation.
Mobile, image-based methods can reduce user burden and bias by automatically
estimating dietary intake from eating occasion images that are captured by
mobile devices. In this paper, we propose an "Energy Density Map" which is a
pixel-to-pixel mapping from the RGB image to the energy density of the food. We
then incorporate the "Energy Density Map" with an associated depth map that is
captured by a depth sensor to estimate the food energy. The proposed method is
evaluated on the Nutrition5k dataset. Experimental results show improved
results compared to baseline methods with an average error of 13.29 kCal and an
average percentage error of 13.57% between the ground-truth and the estimated
energy of the food.

本文提出了一种通过使用 “能量密度地图” 和深度图来自动估算食物能量的技术，同时将其与基线方法相比较，实验结果显示其性能得到了显著提高。