3D Mesh Reconstruction of Foods from a Single Image
Shu Naritomi Keiji Yanai

The University of Electro-Communications, Tokyo
Chofu, Tokyo, Japan{naritomi-s,yanai}@mm.inf.uec.ac.jp

Figure 1: 3D reconstruction results from real food photos with ResNet18, 𝜆3 = 20 (w/ plate consistency loss) and backgrounds.

ABSTRACT
Dietary calorie management has been an important topic in recent
years, and various methods and applications on image-based foodcalorie estimation have been published in the multimedia community. Most of the existing methods of estimating food calorie
amounts use 2D-based image recognition. On the other hand, in
this extended abstract, we would like to introduce our work on 3D
food volume estimation employing a recent DNN-based 3D meshreconstruction technique. We performed 3D mesh reconstructionof a dish (food and plate) and a plate (without foods) from a single
image. We succeeded in restoring the 3D shape with high accuracy while maintaining the consistency between a plate part ofan estimated 3D dish and an estimated 3D plate. To achieve this,the following contributions were made in our recent work [18].
(1) Proposal of “Hungry Networks,” a new network that generates
two kinds of 3D volumes from a single image. (2) Introduction ofplate consistency loss that matches the shapes of the plate partsof the two reconstructed models. (3) Creating a new dataset of 3D
food models that are 3D scanned of actual foods and plates. We
also conducted an experiment to infer the volume of only the foodregion from the difference of the two reconstructed volumes. As aresult, it was shown that the introduced new loss function not only
matches the 3D shape of the plate, but also contributes to obtaining
the volume with higher accuracy. Although there are some existingstudies that consider 3D shapes of foods, this is the first study togenerate a 3D mesh volume from a single dish image. In addition,
we have implemented a web-based 3D dish reconstruction system,“Pop’n Food” [19], which enables reconstruction of 3D shapes from a
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https://doi.org/10.1145/3475725.3483626

single dish image in a real-time way. The demo video of the system
is available at https://youtu.be/YyIu8bL65EE.

CCS CONCEPTS• Information systems → Multimedia content creation.

KEYWORDS
food volume estimation, 3D reconstruction from a single image,food image recognition
ACM Reference Format:
Shu Naritomi Keiji Yanai. 2021. 3D Mesh Reconstruction of Foods from aSingle Image. In Proceedings of the 3rd Workshop on AIxFood (AIxFood ’21),
October 20, 2021, Virtual Event, China. ACM, New York, NY, USA, 5 pages.
https://doi.org/10.1145/3475725.3483626

1

INTRODUCTION

It is necessary to consider the amount of food for accurate estimation of the amounts of food calories for dietary management.
Various methods and applications on image-based food calorie estimation have been published in the multimedia community. Mostof the existing methods of estimating food calorie amounts use
2D-based image recognition. Some methods infer calorie amounts
directly with regression [5, 6], while the others estimate calorie
amounts based on 2D area sizes using detection and segmentation
methods [4, 7]. However, most of the image-based methods cannot
estimate the actual size of foods. Then, size-known reference objects
were commonly used for accurate food calorie estimation. Recently,
some works use AR/MR devices to estimate accurate actual foodsize without a reference object [17, 25]. However, the accuracy ofthe calorie estimation by 2D-based methods is limited due to the
3D nature of real foods. For this reason, there are methods that use
depth estimation [2, 14, 16] or depth cameras [1], but all of themassume that the meal is on a flat plate. Therefore, in this work, we
propose “Hungry Networks,” which is a network for simultaneous
3D reconstruction of both a dish and a plate from a single 2D image.
By using the difference between the estimated volumes of a dishand a plate, we can obtain only the food volume, which is difficult
to obtain in general. To estimate the difference of two volumes, weAIxFood ’21, October 20, 2021, Virtual Event, China

Shu Naritomi and Keiji Yanai

introduce plate consistency loss, which is a new loss function formatching the plate parts of the two output models. Note that we
do not estimate 3D food-only volumes directly, since it is difficult
to create a dataset containing 3D food-only volumes.
Although some existing dietary datasets contain depth images,
none contain a complete 3D shape of foods. Therefore, in our work,
we captured dishes and plates with a 3D scanner, and created a
3D mesh food data set. The corresponding dish image was created by rendering a scanned 3D model. We also experimented withwhether the model learned from the rendered image can be reconstructed from the actual dish image. The contributions in our recent
paper [18] are as follows:• Proposing “Hungry Networks,” a new network that generates
two models from a single image.
• Introduction of plate consistency loss that matches the shapeof the plate part of the two reconstructed 3D models.
• Creating a new dataset of 3D models with 3D scans of real
food and plate.

Figure 2: The overview of “Hungry Networks.”
Table 1: occupancy tabledish occupancy(𝑓𝑑1 (𝑝))0101

2 RELATED WORK
2.1 3D reconstruction from a single image
There are several methods for reconstructing a 3D shape from asingle image regarding 3D representation: voxel-based [3, 27? ],point-cloud-based [8], and mesh-based [10, 11, 20, 23, 28]. Recently,
implicit function-based methods [15, 21, 24] have been proposed,
and they are attracting a lot of attention because of their highrepresentation power and computational efficiency. These methods
eventually apply a marching cube algorithm to extract the mesh.

2.2

Food recognition considering 3D shapes

In this section, we review some works on diets that consider 3D
shape or volume. The ultimate goal of these studies is to estimate
the amounts of calories and ingredients. In Chen et al. [2], a depth
sensor is used to take a depth image to estimate the amount ofcalories in a food. Some methods such as Puri et al. [22] and DietCam [12] obtained a 3D shape by estimating a classical camera
matrix from multiple viewpoints. In recent years, CNN-based has
been acitively explored. Lu et al. [14] generated a depth image using
a neural network and tried to infer the amount of food from thegenerated depth image. Im2calories[16] was trying to estimate thecalorific value by estimating the 3D shape in voxel representationfrom a color image. Ando et al. [1] used depth-camera built in asmartphone to obtain a RGB-D food image for calorie estimation.
Recently, Nutrition5k [26] containing 5,000 RGB-D food images
annotated with nutrition information has been released, which is
the largest open food image dataset having nutrition information.

3

HUNGRY NETWORKS

The Hungry Network is a deep neural network that reconstructs
two 3D shapes of a dish (food and plate) and a plate (without foods)
from a single food image. The network consists of one encoder andtwo decoders as shown in Figure 2. The encoder extracts featuresof a dish image, which consists of a pre-trained backbone network
such as ResNet. Dish image features and 3D points, 𝑝 ∈ R3 , are usedas decoder inputs. The decoders output the occupancy for a dish(containing a food part and a plate part) and a plate, respectively.
The occupancy represents if each of 3D point is inside the meshor outside the mesh with 1/0 binary values. Finally, by applying
the marching cubes algorithm [13] to the occupancy field obtainedby inference with multiple times using decoders, the isosurface isextracted as a mesh.

3.1

plate occupancy(𝑓𝑑2 (𝑝))001
1𝑓𝑑2 (𝑝) − 𝑓𝑑1 (𝑝)
0
-1
1
0

Training

We explain how to train the network. 𝑝 ∈ R3 is the input point,
𝑥 is the feature vector of the input image, and the decoder network for the dish and the plate are represented as 𝑓𝑑1 (𝑥, 𝑝) and𝑓𝑑2 (𝑥, 𝑝), respectively. In addition, the occupancy of training data
is represented by 𝑜 (𝑝) ∈ R corresponding to the point 𝑝. Trainingof occupancy is equivalent to the binary classification problem ofwhether the point is inside or outside the mesh surface. Then, theloss function for learning the occupancy is represented in Eq.1.
Binary cross entropy loss is used for the loss function because it
results in binary classification.
L O (𝑓𝑑 (𝑥, 𝑝), 𝑜 (𝑝)) = L𝑏𝑐𝑒 (𝑓𝑑 (𝑥, 𝑝), 𝑜 (𝑝))(1)

Next, we introduce a plate consistency loss to match the plateparts of both the output mesh models to each other. First, the possible patterns of the combination of occupancy of the corresponding
points on two mesh models are shown in Table 1. When the occupancy of both models at the corresponding point is the same, it
is in the desirable condition. In addition, the condition where theoccupancy of the dish is 1 and the occupancy of the plate is 0 is
no problem, since such a point corresponds to a part of the foodpart of the dish model. On the other hand, the condition where theoccupancy of the dish is 0 and the occupancy of the plate is 1 isproblematic, since this means that inconsistency happens between
the dish model and the plate model, which should be resolved.
Penalties were applied during training only if the dish occupancy
is 0 and the plate occupancy is 1, which corresponds to the condition where 𝑓𝑑2 (𝑝) − 𝑓𝑑1 (𝑝) equals 1 as shown in Table 1. So,max(𝑓𝑑2 (𝑝) − 𝑓𝑑1 (𝑝), 0) is used as a loss function to be minimize.
We will call this “plate consistency loss”.
L C (𝑓𝑑1 (𝑝), 𝑓𝑑2 (𝑝)) = max(𝑓𝑑2 (𝑝) − 𝑓𝑑1 (𝑝) , 0)(2)

The above two formulas (Eq.1, Eq.2) are put together to determine
the loss L B for each mini-batch of the entire learning. Here, B
is the sampled mini-batch, 𝐼𝑖 is the 𝑖-th image of the batch, and𝐾 points are sampled from the 𝑖-th batch, and 𝑝𝑖,𝑗 represents the
sampled 𝑗-th point of the 𝑖-th image. It is assumed that 𝑓𝑒 is theencoder that output image features, and 𝑓𝑑1 and 𝑓𝑑2 are decoder
outputs that output food and plate occupancy rates, respectively.

3D Mesh Reconstruction of Foods from a Single Image

AIxFood ’21, October 20, 2021, Virtual Event, China

5.1

Metrics

For quantitative evaluation, we use Volumetric IoU, Chamber L1
distance, plate consistency, and volume error. The plate consistency
and volume error are proprietary metrics.
The plate consistency is the mean distance from points on thegenerated plate mesh to the nearest neighbor points on the generated food mesh. This value indicates how different the plate part ofthe dish volume is from the plate volume.
Volume error is the mean distance from the inferred volume of
the food region to the ground-truth food volume. The food volume
is obtained by subtracting the plate volume from the dish volume.
On IoU, the higher value is better, while on the other metrics,
the Chamfer L1 distance, plate consistency and the volume error,
the lower values are better.
Figure 3: Images rendered for training without/with backgrounds.
𝑜1, 𝑜2 represents ground truth occupancy value.
𝑥𝑖
𝑦1𝑖,𝑗
𝑦2𝑖,𝑗

=
=
=

𝑓𝑒 (𝐼𝑖 )𝑓𝑑1 (𝑥𝑖 , 𝑝𝑖,𝑗 )𝑓𝑑2 (𝑥𝑖 , 𝑝𝑖,𝑗 )

(3)
(4)
(5)

|B | 𝐾LB =1 ÕÕ
𝜆1 L O (𝑦1𝑖,𝑗 , 𝑜1𝑖 (𝑝𝑖,𝑗 ))|B| 𝑖=1 𝑗=1+ 𝜆2 L O (𝑦2𝑖,𝑗 , 𝑜2𝑖 (𝑝𝑖,𝑗 ))!
+ 𝜆3 L C (𝑦1𝑖,𝑗 , 𝑦2𝑖,𝑗 )4(6)

DATASET CONSTRUCTION

Some existing dietary datasets contain color and depth images [9].
However, no dietary dataset contains 3D Mesh models of foods.
Therefore, for this work, we had to create a new 3D dietary dataset.
We created the dataset consisting of 240 3D models of foods and
38 models of plates. To create the models, we used a commerciallyavailable 3D sensor called “Structure Sensor” and a dedicated 3D
scanning application. Since the same plate was used for different
dishes, the number of plate models is smaller than that of dishmodels.

4.1

Generating input images by rendering

In this work, we rendered images for learning using software called
blender. 25 images were rendered for each model, taken from various angles. In this work, we collected textures of various types oftables and tablecloths from the Web as the background of the rendered dish images, and created composite images. Figure 3 shows
the image created by rendering. The top two lines are just rendered,and the bottom two lines are a composite of the background.

5

EXPERIMENTS

We made experiments with the proposed model, “Hungry Networks”, on the following conditions: (1) we set three values as theplate consistency loss weight (𝜆3 in Eq.6), (2) we train the modelwith rendered dish images with/without backgrounds. To train the
proposed network, we used 216 models for training and 24 models
for evaluation among 240 models in the constructed dataset. The
hyperparameters, 𝜆1, 𝜆2, were fixed at 1, and only 𝜆3 was changedin the experiments. We used Adam as an optimizer.

5.2

Quantitative evaluation

First, we investigated the effect of 𝜆3 on evaluation. The encoder
was based on ResNet34, and training images without backgrounds
were used for training and evaluation. We made experiments with
0, 20 and 50 for 𝜆3 . Note that 0 means we did not used the plate
consistency loss. The results are shown in Table 2. As a result,it indicates that the volume error is greatly reduced when plate
consistency loss is used. On the other hand, the 3D meshes of dishes
and plates were estimated the most accurately without the plate
consistency loss. However, as shown in Figure 5, in case of no plate
consistency loss invisible parts of the dish volume and the plate
volume were differently reconstructed. Note that the mesh capturedby the 3D sensor contains a lot of noise, unlike the handcrafted
3D models included in ShapeNet [29]. Since both the dish decoder
and the plate decoder were optimized independently using only
independent occupancy loss functions to each other, individual
evaluation tends to become better and integrated evaluation suchas volume error tends to become worse.
In the next experiments, we evaluated how much accuracy wasaffected by backgrounds of training images. We used 𝜆3 = 20 with
ResNet18 and ResNet50 as backbones. With backgrounds in the
training images, we achieved the best results regarding the volume
error and the plate consistency.

5.3

Qualitative evaluation

Figure 4 shows the estimated 3D meshes of both dishes and plateswith 𝜆3 = 20, ResNet18 and training images without background.
The 3D meshes of both the dishes and the plates were correctly
estimated for the corresponding images. In addition, we can seethat most of the plate parts of the dish meshes were identical to theplate meshes.
Figure 1 shows the results of using a real food photo as an input
image for a network trained on ResNet18, 𝜆3 = 20, with a background image. Although real images of actual foods were not used
for network training, the trained model was able to reconstruct 3Dvolumes of the dishes and the plates. Various kinds of the plates
such as square flat plates, rectangular plates, round flat plates, andbowls were successfully reconstructed, although the shape and theheight of each of the plates were different greatly.

6

POP’N FOOD

We have created “Pop’n food” as an application to easily view 3Dmodels inferred by the network. In the works on 3D reconstruction,
we often see videos created from images of models rendered fromdifferent angles and displayed in a browser. This application, on
the other hand, uses WebGL via a JavaScript library called three.js,

AIxFood ’21, October 20, 2021, Virtual Event, China

Shu Naritomi and Keiji Yanai

Table 2: The evaluation results with three kinds of 𝜆3 using ResNet34 and non-background images.
𝜆3

IoU (dish)IoU (plate)

0
20
50

0.624
0.550
0.542

0.621
0.607
0.610

Chamfer
L1 (dish)0.01890.02620.0260Chamfer
L1 (plate)
0.01860.01820.0209plate
consistency
0.02560.01680.0152volume error
0.02520.01550.0161

Table 3: The evaluation results with training images with/without backgrounds with 𝜆3 = 20 and ResNet18/50 backbones.
encoderbackground

IoU(dish)

IoU(plate)Chamfer
L1 (dish)

Chamfer
L1 (plate)

ResNet 18
ResNet 50
ResNet 18
ResNet 50

none
none
yes
yes

0.560
0.564
0.565
0.558

0.634
0.617
0.645
0.628

0.02650.02510.02540.02520.01930.01860.01730.0173Figure 4: The estimated volumes of both dishes and plateswith the model trained from non-background images witha ResNet18-based encoder and 𝜆3 = 20.

which allows users to interactively view the 3D model from all
angles.
The user interface of the application is shown in Figure 6. Thereis a list of images on the left and two canvases on the right. The
3D model of the dish is displayed on the upper side of the canvas,
and the 3D model of the plate is displayed on the lower side. Byclicking the button next to the image, the user can see the 3D modelreconstructed from the clicked image.
By loading Hungry Networks when the web server starts anddeploying it on the GPU, the response to the request is within about
2 seconds.

Volume error
0.01500.01470.01460.0157

Figure 6: Web Interface. There is a list of images on the left
and two canvases on the right. The 3D model of the dish isdisplayed on the upper side of the canvas, and the 3D modelof the plate is displayed on the lower side. By clicking the
button next to the image, the user can see the 3D model reconstructed from the clicked dish image. The 3D model isalso rendered in real-time, so you can interact with it (not avideo).

7

Figure 5: Comparative results with/without the plate consistency loss. Note that training condition is the same as Fig.4.

Plate
consistency
score
0.01460.01480.01460.0157

CONCLUSIONSIn this work, we proposed “Hungry Networks” that enabled 3D
shape reconstruction of dishes and plates from a single food image.
For training, we introduced a new loss, plate consistency loss, inorder to maintain the consistency between the plate part of the dishand the plate. In addition, for experiments, we created a dataset
consisting of 3D mesh models of dishes. By the experiments, it was
shown that 3D shapes could be reconstructed with high accuracy
by using rendered images of dishes and composite rendered images of backgrounds for training. In addition, by introducing plate
consistency loss, we succeeded in maintaining and restoring theconsistency of the plate parts of the two meshes, which contributed
to the estimation of the volume of the dietary area. It was shownthat the network learned from the dish images obtained by synthesizing the background image can be correctly reconstructed even if
the real dish image is input as well.
As a future task, the current 3D shape restoration is performed
in a normalized space, and the actual size cannot be taken into
consideration. In order to estimate the amount of calories, it isnecessary to be able to consider the actual size. Therefore, wewould like to use the environment recognition function of the AR
device, RGB-D depth images, reference objects and so on to perform
3D shape restoration considering the actual size, which will lead toaccurate estimation of the amounts of food calorie intake.

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