Dietary Intake Estimation via Continuous 3D Reconstruction of Food
This addresses the need for automated and accurate dietary monitoring to prevent health risks like obesity and diabetes, but it appears incremental as it builds on existing 3D reconstruction techniques.
The study tackled the problem of inaccurate dietary monitoring by proposing a method to estimate food intake via 3D reconstruction from monocular video, showing potential in capturing changes in food volume during consumption.
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.