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With the growth in capabilities of generative models, there has been growinginterest in using photo-realistic renders of common 3D food items to improvedownstream tasks such as food printing, nutrition prediction, or management offood wastage. Despite 3D modelling capabilities being more accessible than everdue to the success of NeRF based view-synthesis, such rendering methods stillstruggle to correctly capture thin food objects, often generating meshes withsignificant holes. In this study, we present an optimized strategy for enablingimproved rendering of thin 3D food models, and demonstrate qualitativeimprovements in rendering quality. Our method generates the 3D model mesh via aproposed thin-object-optimized differentiable reconstruction method and tailorsthe strategy at both the data collection and training stages to better handlethin objects. While simple, we find that this technique can be employed forquick and highly consistent capturing of thin 3D objects.

NutritionVerse-Thin: An Optimized Strategy for Enabling Improved Rendering of 3D Thin Food Models