  77% of adults over 50 want to age in place today, presenting a major
challenge to ensuring adequate nutritional intake. It has been reported that
one in four older adults that are 65 years or older are malnourished and given
the direct link between malnutrition and decreased quality of life, there have
been numerous studies conducted on how to efficiently track nutritional intake
of food. Recent advancements in machine learning and computer vision show
promise of automated nutrition tracking methods of food, but require a large
high-quality dataset in order to accurately identify the nutrients from the
food on the plate. Unlike existing datasets, a collection of 3D models with
nutritional information allow for view synthesis to create an infinite number
of 2D images for any given viewpoint/camera angle along with the associated
nutritional information. In this paper, we develop a methodology for collecting
high-quality 3D models for food items with a particular focus on speed and
consistency, and introduce NutritionVerse-3D, a large-scale high-quality
high-resolution dataset of 105 3D food models, in conjunction with their
associated weight, food name, and nutritional value. These models allow for
large quantity food intake scenes, diverse and customizable scene layout, and
an infinite number of camera settings and lighting conditions.
NutritionVerse-3D is publicly available as a part of an open initiative to
accelerate machine learning for nutrition sensing.
