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arXiv · 2601.20051

Size Matters: Reconstructing Real-Scale 3D Models from Monocular Images for Food Portion Estimation

Gautham Vinod·Bruce Coburn·Siddeshwar Raghavan·Jiangpeng He·Fengqing Zhu
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Abstract

The rise of chronic diseases related to diet, such as obesity and diabetes, emphasizes the need for accurate monitoring of food intake. While AI-driven dietary assessment has made strides in recent years, the ill-posed nature of recovering size (portion) information from monocular images for accurate estimation of ``how much did you eat?'' is a pressing challenge. Some 3D reconstruction methods have achieved impressive geometric reconstruction but fail to recover the crucial real-world scale of the reconstructed object, limiting its usage in precision nutrition. In this paper, we bridge the gap between 3D computer vision and digital health by proposing a method that recovers a true-to-scale 3D reconstructed object from a monocular image. Our approach leverages rich visual features extracted from models trained on large-scale datasets to estimate the scale of the reconstructed object. This learned scale enables us to convert single-view 3D reconstructions into true-to-life, physically meaningful models. Extensive experiments and ablation studies on two publicly available datasets show that our method consistently outperforms existing techniques, achieving nearly a 30% reduction in mean absolute volume-estimation error, showcasing its potential to enhance the domain of precision nutrition. Code: https://gitlab.com/viper-purdue/size-matters

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Gautham Vinod, Bruce Coburn, Siddeshwar Raghavan, Jiangpeng He, Fengqing Zhu. 2026-01-27. Size Matters: Reconstructing Real-Scale 3D Models from Monocular Images for Food Portion Estimation. https://arxiv.org/abs/2601.20051

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