mlsanityData to 14 Oct 2026

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

arXiv 2310.11702 ↗First posted 18 Oct 2023cs.CVcited by 10

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Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 18 Oct 2023.

Builds on

27 of 45 references.

References · 27

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15 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 6.

10 papers.

  1. 2024Food Portion Estimation via 3D Object Scalingcited by 12
  2. 2025Advancing Food Nutrition Estimation via Visual-Ingredient Feature Fusioncited by 5
  3. 2026Food Portion Estimation: From Pixels to Caloriescited by 3
  4. 2026OmniFood8K: Single-Image Nutrition Estimation via Hierarchical Frequency-Aligned Fusioncited by 2
  5. 2025Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Reviewcited by 1
  6. 2026Not Your Stereo-Typical Estimator: Combining Vision and Language for Volume Perceptioncited by 1
  7. 2025PortionNet: Distilling 3D Geometric Knowledge for Food Nutrition Estimationcited by 0
  8. 2026Geometry-Enhanced Portion Estimation for Multimodal LLMscited by 0
  9. 2026Predicting Postprandial Glycemic Response from Meal Images, Clinical Variables, and Gut Microbiome Informationcited by 0
  10. 2026NutriVision: Ingredient-Conditioned Fusion and Prediction for Single-Image Food Nutrition Estimationcited by 0