NutritionVerse-Synth: An Open Access Synthetically Generated 2D Food
  Scene Dataset for Dietary Intake Estimation
Saeejith Nair Chi-en Amy Tai Yuhao Chen Alexander Wong
Manually tracking nutritional intake via food diaries is error-prone and
burdensome. Automated computer vision techniques show promise for dietary
monitoring but require large and diverse food image datasets. To address this
need, we introduce NutritionVerse-Synth (NV-Synth), a large-scale synthetic
food image dataset. NV-Synth contains 84,984 photorealistic meal images
rendered from 7,082 dynamically plated 3D scenes. Each scene is captured from
12 viewpoints and includes perfect ground truth annotations such as RGB, depth,
semantic, instance, and amodal segmentation masks, bounding boxes, and detailed
nutritional information per food item. We demonstrate the diversity of NV-Synth
across foods, compositions, viewpoints, and lighting. As the largest
open-source synthetic food dataset, NV-Synth highlights the value of
physics-based simulations for enabling scalable and controllable generation of
diverse photorealistic meal images to overcome data limitations and drive
advancements in automated dietary assessment using computer vision. In addition
to the dataset, the source code for our data generation framework is also made
publicly available at https://saeejithnair.github.io/nvsynth.