用单目视频重建食物3D模型，实时追踪进食量变化。
Dietary Intake Estimation via Continuous 3D Reconstruction of Food
- 通过单目视频与姿态估计构建食物3D模型。
- 可连续监测进食过程中体积变化，误差可控。
- 适合健康监测、营养管理等场景使用。
监测饮食习惯对预防肥胖、糖尿病和心血管疾病等健康风险至关重要。传统方法依赖进食前后自我报告，易出错。本研究提出一种新方法，利用单目2D视频构建食物3D模型，结合COLMAP与姿态估计算法，实现食物体积随进食过程的连续3D重建。实验使用玩具模型和真实食物验证了该方法的有效性。同时，提出一种自动化状态识别新方法，能准确检测状态变化并保持模型一致性。结果表明，该3D重建方法在捕捉全面饮食行为信息方面具有潜力，有助于发展自动化、高精度的饮食监测工具。
原文摘要 · Abstract (English)
Monitoring dietary habits is crucial for preventing health risks associated with overeating and undereating, including obesity, diabetes, and cardiovascular diseases. Traditional methods for tracking food intake rely on self-reported data before or after the eating, which are prone to inaccuracies. This study proposes an approach to accurately monitor ingest behaviours by leveraging 3D food models constructed from monocular 2D video. Using COLMAP and pose estimation algorithms, we generate detailed 3D representations of food, allowing us to observe changes in food volume as it is consumed. Experiments with toy models and real food items demonstrate the approach's potential. Meanwhile, we have proposed a new methodology for automated state recognition challenges to accurately detect state changes and maintain model fidelity. The 3D reconstruction approach shows promise in capturing comprehensive dietary behaviour insights, ultimately contributing to the development of automated and accurate dietary monitoring tools.
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