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A reasonable and balanced diet is essential for maintaining good health. Withthe advancements in deep learning, automated nutrition estimation method basedon food images offers a promising solution for monitoring daily nutritionalintake and promoting dietary health. While monocular image-based nutritionestimation is convenient, efficient, and economical, the challenge of limitedaccuracy remains a significant concern. To tackle this issue, we proposedDPF-Nutrition, an end-to-end nutrition estimation method using monocularimages. In DPF-Nutrition, we introduced a depth prediction module to generatedepth maps, thereby improving the accuracy of food portion estimation.Additionally, we designed an RGB-D fusion module that combined monocular imageswith the predicted depth information, resulting in better performance fornutrition estimation. To the best of our knowledge, this was the pioneeringeffort that integrated depth prediction and RGB-D fusion techniques in foodnutrition estimation. Comprehensive experiments performed on Nutrition5kevaluated the effectiveness and efficiency of DPF-Nutrition.

DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion