Implicit-Scale 3D Reconstruction for Multi-Food Volume Estimation from Monocular Images

• URL: http://arxiv.org/abs/2602.13041v1
• Date: Fri, 13 Feb 2026 15:52:39 GMT
• Title: Implicit-Scale 3D Reconstruction for Multi-Food Volume Estimation from Monocular Images
• Authors: Yuhao Chen, Gautham Vinod, Siddeshwar Raghavan, Talha Ibn Mahmud, Bruce Coburn, Jinge Ma, Fengqing Zhu, Jiangpeng He,
• Abstract summary: Implicit-Scale 3D Reconstruction from Monocular Multi-Food Images is a benchmark dataset designed to advance geometry-based food portion estimation.<n>This benchmark reframes food portion estimation as an implicit-scale 3D reconstruction problem under monocular observations.
• Score: 21.112563168240737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Implicit-Scale 3D Reconstruction from Monocular Multi-Food Images, a benchmark dataset designed to advance geometry-based food portion estimation in realistic dining scenarios. Existing dietary assessment methods largely rely on single-image analysis or appearance-based inference, including recent vision-language models, which lack explicit geometric reasoning and are sensitive to scale ambiguity. This benchmark reframes food portion estimation as an implicit-scale 3D reconstruction problem under monocular observations. To reflect real-world conditions, explicit physical references and metric annotations are removed; instead, contextual objects such as plates and utensils are provided, requiring algorithms to infer scale from implicit cues and prior knowledge. The dataset emphasizes multi-food scenes with diverse object geometries, frequent occlusions, and complex spatial arrangements. The benchmark was adopted as a challenge at the MetaFood 2025 Workshop, where multiple teams proposed reconstruction-based solutions. Experimental results show that while strong vision--language baselines achieve competitive performance, geometry-based reconstruction methods provide both improved accuracy and greater robustness, with the top-performing approach achieving 0.21 MAPE in volume estimation and 5.7 L1 Chamfer Distance in geometric accuracy.

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