MetaFood3D: 3D Food Dataset with Nutrition Values

## Abstract

Abstract Food computing is both important and challenging in computer vision (CV). It significantly contributes to the development of CV algorithms due to its frequent presence in datasets across various applications, ranging from classification and instance segmentation to 3D reconstruction. The polymorphic shapes and textures of food, coupled with high variation in forms and vast multimodal information—including language descriptions and nutritional data—make food computing a complex and demanding task for modern CV algorithms. 3D food modeling is a new frontier for addressing food related problems, due to its inherent capability to deal with random camera views and its straightforward representation for calculating food portion size. However, the primary hurdle in the development of algorithms for food object analysis is the lack of nutrition values in existing 3D datasets. Moreover, in the broader field of 3D research, there is a critical need for domain-specific test datasets. To bridge the gap between general 3D vision and food computing research, we propose MetaFood3D. This dataset consists of 637 meticulously labeled 3D food objects across 108 categories, featuring detailed nutrition information, weight, and food codes linked to a comprehensive nutrition database. The dataset emphasizes intra-class diversity and includes rich modalities such as textured mesh files, RGB-D videos, and segmentation masks.
Experimental results demonstrate our dataset’s significant potential for improving algorithm performance, highlight the challenging gap between video captures and 3D scanned data, and showcase MetaFood3D’s strengths in high-quality data generation, simulation, and augmentation.

## 1 Introduction

Food is fundamental to our existence, serving not just as a basic necessity for survival but also as a crucial aspect of our social interactions, where sharing images, videos, and even virtual food experiences in video games is commonplace. Food-related image analysis is crucial for monitoring and improving dietary habits across different age groups, as it enables personalized nutrition interventions, supports early detection of dietary deficiencies, and promotes healthier lifestyles tailored to the specific needs of children, adults, and the elderly. In the field of computer vision, food has played a significant role in advancing algorithms, given its frequent occurrence in both specialized and general datasets for tasks such as classification [, , , ], instance segmentation [], and 3D object reconstruction [].

Food data is uniquely complex due to unbalanced classes, intricate textures, hierarchical categorization, and ambiguous shapes.
Often, food images are taken from close distances, with varying camera angles leading to diverse visual representations. Typical single-view-image depictions fall short of providing comprehensive views, obscuring critical details about ingredients and portions. E.g., an overhead image of a sandwich might display only the bun, while a side view could expose the bun, meat, and toppings in greater detail, highlighting the limitations of single-view image analysis.

Accurate measurement is crucial for various food-related tasks, especially under the context of precise dietary assessment, which can serve as a valuable digital biomarker, offering a quantitative and objective measure of an individual’s nutritional intake and its potential impact on their health status. A significant challenge in dietary assessment is to accurately estimate portion sizes from food images []. Various approaches have been developed to tackle this problem, including image based regression [], regression on segmentation masks [, ], mapping to handcrafted 3D shape templates [], 3D reconstruction from multiple images [], and utilizing depth information []. However, the lack of 3D information for individual food object leads to inaccuracies and challenges in generalization. Even with depth data, accurately representing empty spaces beneath food objects remains a challenge, as foods on a plate can exhibit a wide range of 6D poses and stacking relationships.

Recent advancements in 3D vision algorithms, particularly in novel view synthesis [], surface reconstruction [], and 3D object generation [], indicate a promising direction for overcoming these issues. Utilizing 3D methodologies in food-related research offers inherent advantages, such as mitigating challenges posed by varied camera views through novel view synthesis or rendering from learned geometries. These approaches can facilitate the direct computation of food volume per food item for dietary studies, making the process more precise, straightforward, and explainable compared to existing methods. However, at this stage, the main obstacle to applying these 3D algorithms to food-related tasks is the lack of well constructed food datasets.

Many generic large-scale 3D datasets [, , ] have recently been released, fueling the development of 3D vision algorithms [, ]. Yet, there is a notable scarcity of food-specific datasets to train and evaluate 3D algorithms on food-related tasks. Existing 3D datasets with food generally lack dietary annotations such as weight, calories, and other nutrition values, which is crucial for developing 3D or image-based dietary assessment algorithms. Furthermore, there is a shortage of benchmark 3D food datasets featuring diverse intra-class variation. For instance, the OmniObject3D dataset [] includes 2,837 food objects, but the selection of its food instances fails to emphasize the appearance variations within each food category. Many food items in OmniObject3D, such as lemons, exhibit similar appearances and geometries within the same category.

To bridge the gap between general 3D vision and food computing, and to provide a unique benchmark for both general and food-specific downstream tasks, our dataset MetaFood3D (as shown in Figure 1) endeavors to develop a food-specific 3D dataset that advances dietary analysis from 2D to 3D. MetaFood3D includes a total of 637 3D food objects in 108 food categories. Each food object in the dataset is meticulously labeled with detailed nutrition information, weight, and food codes linked to a comprehensive nutrition database []. We emphasize intra-class diversity by collecting foods with varying appearances and nutritional information. Beyond nutritional facts, our dataset includes rich modalities such as textured mesh files, RGB-D videos, and segmentation masks. Additionally, the dataset incorporates hierarchical relationships characterized by specifying sub-food-categories, known as food items, within general food categories, facilitating tasks related to fine-grained classification. Finally, we establish baselines for nutrition estimation, perception, reconstruction, and generation tasks. Our experiments demonstrate that our dataset has significant potential for improving performance and highlight the challenging gap between video captures and 3D scanned data. Furthermore, we show the potential of our dataset for high-quality data generation, simulation, and augmentation by presenting high-quality visual results.

## 2 Related Work

**Table 1: Public Datasets with Real-world Food Objects. “Samples" represents the total number of food data samples in the dataset. Note that we exclude food toys in GSO.**
|  | Multiview/video | Depth | Inst Mask | Mesh | Size Calibration | Nutrition | Food categories | Samples |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Food Specific Datasets |  |  |  |  |  |  |  |  |  |
| Food2K [] (2D) |  |  | ✓ |  |  |  | 2,000 | 1 Million |  |
| ECUSTFD [] (2D) |  |  |  |  | ✓ | ✓ | 19 | 2,978 |  |
| Nutrition5K [] (2D) | ✓ | ✓ |  |  | ✓ | ✓ | 250 | 5,006 |  |
| NutritionVerse3D [] | ✓ |  | ✓ | ✓ |  | ✓ | 54 | 105 |  |
| Generic 3D Datasets |  |  |  |  |  |  |  |  |  |
| GSO [] |  |  | ✓ | ✓ | ✓ |  | 0 | 0 |  |
| CO3D [] | ✓ | ✓ | ✓ |  | ✓ |  | 10 | 5,077 |  |
| OmniObject3D [] | ✓ |  | ✓ | ✓ | ✓ |  | 85 | 2,837 |  |
| Ours | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | 108 | 637 |  |

In this section, we provide detailed reviews of related food and 3D object datasets and a brief review of relevant downstream tasks. The features of these datasets are summarized in Table 1.

Food Datasets are primarily developed to answer key questions in food computing: “What is the food in the image?”, “What is the portion size?”, and “What is the nutritional content of the food?”. While numerous food classification datasets exist, ranging from the classic Food-101 dataset [] to the latest Food2K dataset [], datasets for portion estimation or macro-nutrient estimation are significantly fewer.
This scarcity is due to the complexity and labor-intensiveness of collecting multi-modal data with physical food object references. Numerous efforts have been undertaken to mitigate the need for gathering data on physical objects. These include leveraging images and metadata from recipe websites [] or creating synthetic data by pasting image textures onto predefined geometries []. However, these approaches have fundamental flaws, as the relationship between the food appearance and the food weight is not validated by real food items.
Despite various proposals for ground-referenced food portion estimation datasets in existing literature [, , , ], only three datasets that include nutrition values are publicly available: ECUSTFD [], Nutrition5K [], and NutritionVerse3D []. The ECUSTFD dataset contains no geometry information. In the Nutrition5K dataset, food items are mixed together without segmentation masks, making it infeasible to perform nutrition and geometric modeling for individual food items. The NutritionVerse3D dataset, which includes models from FoodVerse [], is small-scale, containing 105 3D food models across 42 unique food types. The food items are not calibrated in size and the selection of food types appears to be random and imbalanced.

3D Object Datasets focus either on synthetic objects created by humans or on real-world objects that are manually scanned. Synthetic object datasets, such as ShapeNet [] and Objaverse [],
are unsuitable for dietary assessment applications due to their artistic object appearances and non-referenced scales.
Real-world scanned objects offer realistic appearances and geometry, but many real-world 3D object datasets primarily focus on non-perishable commercial household items, including Google Scanned Objects (GSO) [], CO3D [], YCB Objects [], AKB-48 [], and MetaGraspNetV2 []. Some real-world scanned object datasets do include food items, but they often suffer from limitations such as a small number of food categories []. Additionally, the selection of food items is often random and does not reflect the distribution of commonly eaten foods, leading to bias in dietary assessment [].

Food Data Analysis for Dietary Assessment:
Existing food portion and nutrition value estimation methods can be classified into four main categories: stereo-based [, ], depth-based [, ], model-based [, ], and neural network-based methods [, , , , , , ].
Recently, a 3D model-based method [] has demonstrated the importance of 3D models in food portion estimation by outperforming many existing methods.

3D Point Cloud Perception: This task seeks to classify point cloud data composed of a set of 3D coordinates. PointNet [] was first proposed to directly process unordered raw point cloud sets. PointNet then led to the development of new models [, , , ]. Due to the characteristics of real-world point cloud data, robustness is crucial in 3D point cloud perception. Previous works [, , , ] have studied the robustness of models on point cloud data from different domains and standardized corrupted dataset.

Novel View Synthesis and 3D Mesh Reconstruction: Novel view synthesis aims to generate high-quality images from new perspectives given only a few training images. Neural Radiance Fields (NeRF) [] addresses this problem by training a multilayer perceptron (MLP) network to predict the color values and densities of locations in space. Recent advancements have tackled issues related to aliasing, quality, and efficiency [, , , ].
3D mesh reconstruction aims to recreate the mesh of an object. Traditional methods like Structure from Motion (SfM) [] achieve this by determining the camera pose associated with each image. Recent approaches leverage the success of volume rendering in novel view synthesis [, , ] or employ Neural Signed Distance Fields [].

3D Generation: With advancements in novel view synthesis and generative models [], numerous text-to-3D generation methods have emerged in the past year []. A typical pipeline involves leveraging diffusion models to generate multi-view images of an object, which are then utilized in 3D reconstruction methods to create the 3D model [, ]. Other approaches focus on learning Neural Signed Distance Fields to achieve 3D generation [] .

## 3 Dataset

The primary focus of creating the MetaFood3D dataset is to ensure its suitability for dietary assessment applications by carefully selecting food objects and choosing data modalities and capture methods that closely resemble real-world scenarios. Figure 2 provides an overview of MetaFood3D, illustrating the distribution of data and energy content across food objects, as well as the intra-class variance of the collected food objects. In the remainder of this section, we detail the process of curating the food list, the data collection pipeline, post-processing steps, and discuss the limitations of the dataset.

Food List: The food list for our 3D food dataset was generated by extending and enhancing the food categories from the VIPER-FoodNet (VFN) dataset [], which contains 74 of the most frequently consumed foods in the United States. The VFN dataset was created based on the What We Eat In America (WWEIA)(^†^††https://data.nal.usda.gov/dataset/what-we-eat-america-wweia-database) database, which provides two days of 24-hour dietary recall data collected through in-person and follow-up phone interviews. The food categories in the VFN dataset were selected based on their high intake frequency, ensuring that the dataset represents the most commonly consumed foods in the United States. Recent studies have demonstrated the widespread application of the VFN dataset in various domains, including long-tailed learning [, ], continual learning [], personalized classification [], and multi-modality learning []. In this work, we expanded the original 74 food categories from the VFN dataset by selecting an additional 34 food categories based on the most frequently consumed foods in U.S. according to the NHANES survey data [], resulting in a total of 108 food categories in the MetaFood3D dataset. This expansion allows for a broader representation of the food landscape and provides more diverse data for dietary assessment and computer vision tasks. One of the key enhancements in our dataset compared to the VFN dataset is the granularity of food code matching. In the VFN dataset, each food category is matched with a single general 8-digit food code from the Food and Nutrient Database for Dietary Studies (FNDDS) [].
However, in our work, we take a more detailed approach by identifying each food object with a specific FNDDS food code. For example, "Pie" is a food category in our dataset, we match all the food objects within this category with specific food item such as "Pie, chocolate cream," "Pie, pecan," "Pie, apple," and "Pie, lemon," and assign them their respective specific FNDDS food codes. This detailed matching process allows for a more accurate representation of the diverse food items within a given food category, as each food item has its unique ingredients and nutritional profile. By providing this level of detail, our 3D food dataset enables researchers and practitioners to perform more precise dietary analysis and develop more sophisticated computer vision algorithms that can distinguish between different food items within a given food category. The fine-grained categorization of food items within each food category, results in a total of 220 food items, each with a unique FNDDS food code. Overall, the establishment of this food list with food items serves as the foundation of our 3D data collection process. By identifying and including various food items within each food category, the collected 3D models will capture the intra-class diversity in terms of visual and geometric within a food category, enabling the development of more accurate algorithms to advance dietary assessments.

Figure: Figure 2: The distribution of MetaFood3D, which includes 108 mostly consumed food categories with high intra-class diversity, a total of 220 unique food items, each matched to a unique food code, and 637 single food objects in total with each containing nutrition values annotations.
Refer to caption: x2.png

Data Collection: We prioritize sourcing real-world food objects from restaurants and ready-to-eat or frozen foods from grocery stores. For food that are difficult to source, we prepare them from raw ingredients such as peanut butter and jelly sandwich.
Besides leveraging both the food category and food item categorization, we also enhance intra-class diversity during the data collection step by employing various food sourcing strategies. These include sourcing food from different restaurants, stores, or locations; selecting diverse flavors, brands, breeds, or forms; cutting, peeling, or unwrapping the food; and preparing the food with different ingredients. These strategies ensure that our dataset captures a wide range of appearances and geometries for each food category.
Our 3D data collection follows a similar approach to OmniObject3D [] and NutritionVerse3D []. The food object is placed on a turntable and scanned by a 3D scanner, the Revopoint POP 2(^‡^‡‡https://www.revopoint3d.com/pages/face-3d-scanner-pop2), which is positioned statically on a tripod. We then record the food’s weight and nutrition value. For most objects, keypoint tracking provided by RevoScan software (^§^§§[https://global.revopoint3d.com/pages/revoscan5](https://global.revopoint3d.com/pages/revoscan5)) is sufficient to obtain a 360-degree point-cloud capture of the food object. If the scan is not successful, we manually turn the object.
Unlike OmniObject3D [], which captures a 360^$\circ$ range, we perform a 720^$\circ$ RGBD video capture by rotating the object twice in a spiral motion, ending with an overhead capture. This approach ensures that we capture the most likely camera angles from typical smartphone users. If the food object can be flipped (e.g., a bowl of beef stew cannot be flipped), we flip the object and repeat the data capture process to capture the underside of non-fluid objects. The depth measurement is obtained using an iPhone App called Record3D (^¶^¶¶[https://record3d.app/](https://record3d.app/)). To ensure precise scale and color measurements, we use calibration fiducial markers [] for both camera angle and color calibration.

Annotation: After collecting the 3D food objects, we perform a series of postprocessing steps and annotate each food object. One of our unique contributions is the annotation of weight and nutrition facts for each food object, which is crucial for food data and dietary assessment tasks. During the data collection process, we record the weight $w_{i}$ (in grams) of each food object $i$. By leveraging the food code associated with each object, we obtain the nutrient value density $d_{i}$, which represents the nutrient content per 100 grams of the food item. The nutrient value density is typically expressed as a vector $d_{i}=[e_{i},p_{i},c_{i},f_{i}]$, where $e_{i}$, $p_{i}$, $c_{i}$, and $f_{i}$ denote the energy (in kilocalories), protein (in grams), carbohydrates (in grams), and fat (in grams) per 100 grams of food item $i$, respectively.
Given the weight $w_{i}$ and nutrient value density $d_{i}$, we can accurately determine the total nutrient content $n_{i}$ for the specific quantity of food object $i$ in our dataset with $n_{i}=\frac{w_{i}}{100}\cdot d_{i}$. The inclusion of weight and nutrition values enables researchers to develop and evaluate algorithms for precise dietary assessment and nutrient estimation. Similarly as in [], we also generate data to support various general 3D vision research topics such as point cloud analysis, neural radiance fields, and 3D generation. This includes rendering object-centric and photo-realistic multi-view images using Blender (^∥^∥∥[https://www.blender.org](https://www.blender.org)) with accurate camera poses, generating depth and normal maps, and sampling multi-resolution point clouds from each 3D model. Additionally, we provide uniformly sampled video frames with corresponding segmentation masks and depth information. The segmentation masks are generated based on GroundingDINO [], Segment Anything Models (SAM) [] and Cutie [].

Overall, We collected 637 food objects with 108 food categories. Each food object in our dataset includes the following labels: a scanned 3D object mesh with texture, RGBD video capture of the food both in a standard pose and flipped (if applicable), depth images and masks corresponding to the RGBD video captures, FNDDS food code, nutrition value (energy, protein, carbohydrates, fat), weight value, Blender-rendered frames with normal and depth images, camera parameters used for rendering, and fiducial marker (with known physical dimensions) used in the video capture.

Limitations: Our food list is selected from the American diet, thus it may not accurately represent the diversity of diets in other regions of the world. Additionally, due to the perishable nature of food objects and the labor-intensive processes of data sourcing, collection, and post-processing, the scale of our dataset remains small compared to other food image and 3D object datasets.

## 4 Experimental Results

In this section, we demonstrate the usage of the MetaFood3D dataset in four downstream tasks: 3D food perception (Section 4.1), novel view synthesis and 3D reconstruction (Section 4.2), 3D food generation and rendering (Section 4.3), and food portion size estimation (Section 4.4).

**Table 2: Robustness Analysis on Intra-class Diversity and Point Clouds Corruption**
|  | OA${}_{\text{Uniform}}$ $\uparrow$ | OA${}_{\text{Diverse}}$ $\uparrow$ | OA${}_{\text{Clean}}$ $\uparrow$ | mCE $\downarrow$ |
| --- | --- | --- | --- | --- |
| DGCNN [] | 0.862 | 0.198 | 0.725 | 1.000 |
| PointNet [] | 0.822 | 0.179 | 0.672 | 1.210 |
| PointNet++ [] | 0.893 | 0.214 | 0.761 | 0.912 |
| SimpleView [] | 0.919 | 0.219 | 0.747 | 0.992 |
| GDANet [] | 0.903 | 0.206 | 0.740 | 0.935 |
| PAConv [] | 0.892 | 0.199 | 0.711 | 1.036 |
| CurveNet [] | 0.906 | 0.222 | 0.745 | 0.966 |
| RPC [] | 0.900 | 0.215 | 0.738 | 0.959 |
| PointMLP [] | 0.912 | 0.231 | 0.756 | 1.033 |
| Point-BERT [] | 0.914 | 0.226 | 0.729 | 1.013 |

### 4.1 3D Food Perception

Intra-class Diversity of Food Shapes: Food objects in real-world settings are often processed into various shapes, such as whole fruits versus sliced fruits or a single nut compared to multiple nuts in a bowl.
To demonstrate the impact of shape diversity on 3D perception algorithms, we select and train 10 existing methods on OmniObject3D and evaluate their performance on both OmniObject3D (OA${}_{\text{Uniform}}$) and MetaFood3D (OA${}_{\text{Diverse}}$) using shared food categories. Overall Accuracy (OA) is used to measure the models’ robustness against diverse point cloud shapes.
Table 2 shows that OA${}_{\text{Diverse}}$ was generally 70% lower than OA${}_{\text{Uniform}}$, indicating that models trained with relatively uniform shapes achieved significantly degraded performance on diverse-shaped food test set. This finding highlights the importance of incorporating shape diversity in 3D food datasets, a key strength of MetaFood3D, ensuring the robustness and generalizability of 3D perception algorithms in real-world applications.

Corruption in Point Clouds: Real-world 3D point clouds of food items can be affected by various types of corruptions, such as noise, missing points, or scaling issues, arising from factors such as sensor limitations, or variations in scanning conditions. To evaluate the robustness of 3D perception models under these corruptions, we created MetaFood3D-C by modifying MetaFood3D with common corruptions described in []. OA${}_{\text{Clean}}$ represents the overall accuracy on the clean MetaFood3D test dataset. The mean Corruption Error (mCE) [] corresponds to the models tested on the MetaFood3D-C to assess their performance in the presence of real-world corruptions. As shown in Table 2, PointNet++ and GDANet demonstrate the best robustness on average against various corruptions.

### 4.2 Novel View Synthesis and 3D Mesh Reconstruction

Figure: Figure 3: Reconstructed Mesh: (a) Ground-truth textured 3D mesh of a complex food item (nachos). (b) A textured 3D mesh of the same food item (nachos) reconstructed from video using Nerfacto. (c) and (d) are mesh-only views of the ground truth and the reconstructed model respectively.
Refer to caption: x3.png

**Table 3: Novel view synthesis results on 108 categories. “Render” represents rendered Blender data from ground truth meshes and “Video” represents captured video data.**
| Method | Input | PSNR ($\uparrow$) | SSIM ($\uparrow$) | LPIPS ($\downarrow$) |
| --- | --- | --- | --- | --- |
| Nerfacto [] | Render | 19.00 | 0.9162 | 0.0896 |
| Video | 22.81 | 0.9611 | 0.0718 |  |
| Nerfacto (masked) [] | Render | 18.87 | 0.9170 | 0.0909 |
| Video | 9.09 | 0.0557 | 1.0684 |  |
| Gaussian Splatting [] | Render | 51.24 | 0.9975 | 0.0044 |
| Video | 37.75 | 0.9890 | 0.0118 |  |

In dietary assessment applications, participants are expected to take minimal actions when capturing food-related media, such as recording a short video with limited food pose coverage. These applications serve as ideal test grounds for Novel View Synthesis and 3D Mesh Reconstruction algorithms. In this section, we present preliminary results for these two tasks using both video captures and Blender-rendered images. For novel view synthesis, we select one object per category and apply recent algorithms, Nerfacto [] and Gaussian Splatting (GS) [], using their official code under default settings. The models are trained on 90% of the data and tested on the remaining 10%. We follow [] and report PSNR, SSIM, and LPIPS scores.
The results are summarized in Table 3.
Upon inspecting the visual results, we observe that Nerfacto struggles with our dataset. In some video-captured scenes, Nerfacto fails to learn the foreground object, resulting in only a pure background color, whereas GS successfully synthesizes all objects.
We further tested the Nerfacto method by providing it with foreground masks. Visually, we observed that the foreground was correctly learned, but this approach created artifacts in the background, leading to poor quantitative results as shown in Table 3. Therefore, masking plays a crucial role for the Nerf-based method, Nerfacto, on video data but not on rendered data. This discrepancy highlights the challenging non-uniform sparse views and object scale variations in our video data.
For 3D mesh reconstruction, we apply Nerfacto with surface normal prediction settings. Poisson surface reconstruction is then applied to the trained Nerfacto model to obtain the reconstructed mesh. The predicted object meshes from rendered images are compared to the original meshes using Chamfer distance (CD). However, 5 out of 108 objects fail to reconstruct, while the remaining meshes have an average CD of 903.65. For video data, we only provide one of the qualitative results in Figure 3 due to the labor-intensive process of pose alignment with the scanned ground truth object. These results underscore the challenging nature of our dataset.

Figure: Figure 4: MetaFood3D utilizes GET3D [] to generate a diverse array of food objects.
Refer to caption: x7.png

**Table 4: Qualitative results for different generated food objects with volume and energy estimates**
| Food object | Volume (cm^3) | Energy Estimate (kCal) | FID ($\downarrow$) | CD x $10^{3}$ ($\downarrow$) |
| --- | --- | --- | --- | --- |
| Pizza | 129.83 | 186.37 | 76.09 | 7.10 |
| Banana | 274.69 | 260.01 | 94.26 | 10.75 |
| Apple | 278.88 | 217.36 | 105.55 | 5.45 |
| Frankfurter Sandwich | 898.01 | 501.44 | 99.81 | 4.69 |
| Donuts | 315.03 | 578.60 | 93.15 | 4.44 |
| Grapes | 436.15 | 226.22 | 118.36 | 14.82 |
| Beef stew | 508.73 | 360.88 | 163.62 | 30.60 |
| Bagel | 326.04 | 308.58 | 129.01 | 58.48 |
| Pancake | 358.24 | 1205.26 | 106.11 | 42.60 |
| Grilled Salmon | 202.20 | 573.98 | 108.19 | 18.56 |

### 4.3 3D Food Generation and Rendering

Our MetaFood3D dataset enables the generation of highly realistic 3D food objects and facilitates the enrichment of existing 2D food datasets through innovative data synthesis techniques.
For 3D object generation, we use GET3D [] to generate textured 3D meshes for various food categories in our dataset. We train the GET3D model from scratch for each selected food type separately, using 3,500 epochs and an average of 750 rendered images per object at a resolution of 512. To compensate for the smaller initial object count compared to the dataset used in GET3D, we set the gamma value to 3,000, penalizing the discriminator and encouraging the generation of more realistic meshes.
We demonstrate the quality of the generated objects through FID [] and Chamfer Distance (CD)[] as shown in Table4. A unique aspect of our 3D generation is the inclusion of volume and energy estimates for each generated food object. The energy estimates are calculated based on the generated object’s volume, determined using Blender, and the corresponding FNDDS food codes provided by our dataset’s nutrition values.
This enhances the realism of the generated objects, enables accurate energy calculations, and improves dietary assessment functionalities.
Figure 4 visualizes our 3D generation that feature natural textures and coherent shapes enriched by geometric details.

Figure: Figure 5: (a) Synthetic scene generation in NVIDIA Omniverse, composed using individual food objects from MetaFood3D. This scene displays a breakfast plate with associated nutrition values for each item including a total weight of 1,433g, 1,944kCal energy, 70g protein, 103g fat, and 191g carbs. (b) Depth map. (c) Instance segmentation mask. (d) 3D model of an avocado from MetaFood3D, characterized by a brown and dull skin texture. (e) The same avocado mesh as in (d), enhanced with a new texture file generated using Text2Tex [] with the prompt: avocado.
Refer to caption: x8.png

MetaFood3D also allows researchers to generate fully customized 2D synthetic food data by leveraging its collected or generated high-quality 3D food objects, detailed nutrition values, and realistic texture generation capabilities. The dataset supports the creation of synthetic eating scenes with adjustable parameters such as food item placement, portion sizes, and nutrition composition. As shown in Figure 5 (a)(b)(c), we create a breakfast scene in NVIDIA Omniverse simulation engine (^**^****[https://developer.nvidia.com/isaac-sim](https://developer.nvidia.com/isaac-sim)), complete with ground truth labels such as nutrition values, segmentation masks, and depth map. Additionally, the ground truth of bounding boxes and object 6D poses can also be extracted. These scenes can be automatically generated, as described in []. Furthermore, texture generation techniques [] can be leveraged to augment food appearances as shown in Figure 5 (d)(e).

### 4.4 Food Portion Estimation

The food portion estimation is a challenging yet important task for food image analysis. Leveraging the rich nutrition value annotations and 3D information in the MetaFood3D dataset,
we compare the performance of different portion estimation methods covering the four major approaches (stereo-based, depth-based, model-based, and neural network-based) as discussed in Section 2. Specifically, we sample 2 frames from the captured video for each food item in the dataset. The food items are divided into training and testing sets, with one food item per category in the testing set and the remaining items in the training set. Overall, the training set contains 1,036 images, while the testing set consists of 216 images. All methods are evaluated on the same testing set for a fair comparison. We compare the methods using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE).
We use V-MAE and V-MAPE for volume estimation (cm^3), and E-MAE and E-MAPE for energy estimation (kCal). Neural network-based methods directly regress energy values, so V-MAE and V-MAPE are not available for them.

**Table 5: Comparison of image-based dietary assessment methods on the MetaFood3D dataset.**
| Method | V-MAE | V-MAPE | E-MAE | E-MAPE |
| --- | --- | --- | --- | --- |
| Baseline | 151.85 | 845.69 | 221.37 | 1287.25 |
| Stereo Reconstruction [] | 135.96 | 210.90 | 271.78 | 244.55 |
| Voxel Reconstruction [] | 123.34 | 104.07 | 190.38 | 145.31 |
| RGB Only (ResNet50) [] | - | - | 1932.01 | 1124.9 |
| Density Map Only (ResNet50) [] | - | - | 1100.39 | 663.43 |
| Density Map Summing [] | - | - | 436.12 | 142.44 |
| 3D Assisted Portion Estimation [] | 195.92 | 79.33 | 260.79 | 102.25 |

The results presented in Table 5 demonstrate the performance of different classes of existing methods on our MetaFood3D dataset. The 3D Assisted Portion Estimation method [] achieves the lowest V-MAPE and E-MAPE. However, it is important to note that this method has higher V-MAE and E-MAE values compared to some other approaches, which suggests that the incorporation of 3D object information can lead to improved performance in terms of percentage errors, but further research may be needed to reduce the absolute errors. The MetaFood3D dataset provides a valuable resource for developing and evaluating various dietary assessment techniques, and the presented results highlight the potential for further improvements in food portion estimation accuracy.

## 5 Conclusion

In this paper, we present MetaFood3D, a food-specific 3D object dataset to advance dietary analysis. This new dataset provides a robust benchmark for developing and evaluating 3D vision algorithms for real-world scenarios. The dataset features diverse intra-class variations, detailed nutrition annotations and rich multimodal data.
Experimental results demonstrate great potential for using our dataset in various downstream tasks related to food image analysis.

## Peer Reviews

confidence: 4
contribution: 3
initial_rating: 6
presentation: 3
questions: Table 4 is included but doesn't have a discussion in the main text. What do the numbers shown in the table mean?
It would be great if the authors can discuss the main points that were raised in the weaknesses section.
soundness: 3
strengths: - The dataset covers all important aspects of a dataset that can be used for estimating properties like portion size or nutrition content: multi-view images, instance masks, size and nutrition information.
- The most important distinction to prior work is the high diversity and large scale while also including 3D information. This is important for nutrition estimation and portion size estimation, as these applications require absolute measurements.
- The evaluations of what the dataset can be used for are thorough, covering 3D recognition, 3D reconstruction, 3D generation and food image/scene rendering, and portion size estimation.
- For the task of portion size estimation, empirical evidence is presented that having access to 3D information can improve performance
- The paper is well written and easy to follow, and the decisions behind the data collection process are explained and justified well.
summary: This work presents MetaFood3D, a dataset of 3D scans of real-world food items. The dataset consists of about 600 food samples from 108 categories, with multi-view images, depth, instance masks, meshes, size calibration and nutrition information. Datasets such as this are important for developing and evaluating computer vision systems for important capabilities such as nutrition estimation and portion size estimation. It is the largest dataset to date of its kind.

The paper includes empirical investigations on 3D recognition, 3D shape reconstruction, 3D food generation/rendering and food portion estimation, showcasing the utility of the dataset. The proposed dataset is an important contribution enabling future work in this domain.
weaknesses: - The paper would be greatly improved if in addition to a dataset, there was a well designed benchmark specifically built around applications like nutrition or portion size estimation, including in the wild testing data collected with various smartphones in various contexts. This would tie it more closely
- The application of "food generation" seems a bit arbitrary. It would be very helpful if there was some explanation//justification as to why this is useful and what is the impact of being able to generate high quality 3D food assets.


Minor:
- Citation typos on L279 and 280

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confidence: 3
contribution: 2
initial_rating: 5
presentation: 3
questions: - Can we trust the information about calories annotated in the dataset?
soundness: 3
strengths: The dataset fills a gap in the field by providing 3D food data with detailed nutritional annotations. The authors benchmark different tasks using the proposed dataset.
summary: The paper introduces MetaFood3D dataset to aid in dietary assessment and computer vision tasks. This dataset consists of 637 3D-scanned food items across 108 categories, each annotated with nutritional values, weight, and food codes together 3D meshes, segmentation masks, depth maps, etc. Experiments show that the proposed dataset supports different downstream tasks, including 3D food perception, novel view synthesis, 3D food generation, and portion estimation.
weaknesses: - The food categories reflect only American dietary patterns, which may limit the dataset’s applicability for global diets.

- The dataset includes only 637 objects, which may restrict the generalizability of models trained on it, especially for categories with high visual variability.

- The novelty is limited, which is not enough for ICLR conference. The authors only propose the dataset and benchmarks using existing methods. There is no contribution about proposing methods.

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confidence: 4
contribution: 3
initial_rating: 5
presentation: 3
questions: 1. How does the dataset handle seasonal variations in food items, which could affect the accuracy of dietary assessments over different periods?
2. What measures are in place to ensure the long-term maintenance and updating of the dataset to reflect changes in food types and nutritional values?
3. Could the authors elaborate on the potential of MetaFood3D for applications beyond dietary assessment, such as in the food industry or culinary arts?
4. The authors referenced classic Food-101 dataset in “Related Work”, why there is no comparison in Table 1?
5. In figure 2, the images shown on the 3D Food Data Distribution have no correspondence with the food names on the abscissa. Please clarify it.
6. What’s the difference among the objects in the same category? Is there a standardization to select them? I checked the supplementary files, for example, I didn’t see much difference between point clouds in “Apple” category.
7. For the can-be-flipped objects in section 3 “Data Collection”, since they have been captured twice, will it be two meshes provided? Or is there a way to integrate both captured data to make a better 3D mesh?
8. In table 3, the experiment with masks shows poorer performance, the authors did explain the reason, but they also mentioned “masking plays a crucial role for the Nerf-based method”. To support this point, it is better to calculate the metric with rendered results with masks to focus on the foreground.
9. There is no visual results about 3DGS, please add them, as well as more Nerfacto’s results.
10. In section 4.2, why there is no evaluation on 2DGS, since authors have already mentioned it in “Related Work”?
11. In section 4.2, except for Poisson surface reconstruction, I think it is worth to try nerf2mesh [Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement.] to reconstruct 3D mesh.
12. In section 4.2, why there is no mesh reconstruction based on gaussian splatting’s results? As I know, there are some algorithms such as 2DGS and SuGaR [Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering] could do the reconstruction work.
13. For the “volume” mentioned in table 4, can you give a detailed description on how to calculate it?
14. The authors presented more detailed food code representation to enable more precise dietary analysis. However, there is no comparative experiments between previously used food code and new code in the paper, to make the conclusion more solid.
15. For “challenges in data collection” in the appendix, it is better to provide visual examples with textual description.
soundness: 3
strengths: 1. It is interesting to establish a 3D dataset for food, which also provides different sources, such as textured meshes, RGB-D videos, and segmentation masks.
2. It provides multiple downstream tasks, including 3D food perception, novel view synthesis, 3D mesh reconstruction, 3D food generation, and food portion estimation.
summary: This paper introduces a new dataset MetaFood3D, which aims to advance the dietary analysis from 2D to 3D. It includes 637 3D food objects across 108 categories with rich modalities, such as textured mesh files, RGBD videos, and segmentation masks. This paper demonstrates four downstream tasks: 3D food perception, novel view synthesis and 3D reconstruction, 3D food generation and rendering, and food portion size estimation.
weaknesses: 1. The food list is derived from the American diet, which may limit its applicability and representation of dietary diversity in other regions.
2. Compared to existing datasets, this dataset seems to be not large enough in scale, and its design does not sufficiently highlight the characteristics of the food category.

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confidence: 2
contribution: 2
initial_rating: 5
presentation: 3
questions: I like the direction of this paper and I agree with how important a food dataset can be. However, I think there are more work needs to be done to make the dataset larger scale and/or with more accurate annotations.
soundness: 3
strengths: - A multi-modal dataset on food categories is important in the relevant community.
- According to Table 1, the proposed dataset seems to include more types of annotations than any prior datasets.
- Presentation is mostly clear. Important stats are provided as well.
- A major contribution of this paper is its thorough evaluation of the proposed dataset, with a total number of 4 different tasks.
summary: This paper proposes a dataset for food in particular. Given the rich type of annotations the dataset provides, various experiments/models are benchmarked on the dataset.
weaknesses: - My major concern is the scale of the proposed dataset. Although there are 108 different food categories, there are only 637 samples. Compared to OmniObject3D which does not provide nutrition information, the proposed dataset yields only ~20 more categories but with <1/4 number of samples. This limits the contribution and future potential of the dataset --- it can only be used for evaluation not for training. 
- There is no new method proposed along with the dataset, which weakens the contribution further. Again, this could be due to the reason that the dataset can not be used for training, which limits its use case and prevents specialized methods from being proposed.  
- Some of the annotations are not really ground truth. I could be wrong, but according to my understanding, annotated depth maps are obtained via blender, which corresponds to the reconstructed 3D model rather than the actual depth captured by some sensor. Moreover, segmentation masks seem to be inferred from off-the-shelf models directly rather than being manually labeled. These raise concerns about the accuracy of the annotations.