DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion
This work addresses the challenge of automated dietary monitoring for health-conscious individuals, representing an incremental advancement in food nutrition estimation methods.
The paper tackled the problem of limited accuracy in monocular image-based food nutrition estimation by proposing DPF-Nutrition, which integrates depth prediction and RGB-D fusion, achieving improved performance on the Nutrition5k dataset.
A reasonable and balanced diet is essential for maintaining good health. With the advancements in deep learning, automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient, and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition.