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Food Volume Estimation Based on Deep Learning View Synthesis from a Single Depth Map.

Frank P-W Lo1, Yingnan Sun2, Jianing Qiu3

  • 1Hamlyn Centre, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK. po.lo15@imperial.ac.uk.

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Summary
This summary is machine-generated.

This study introduces a deep learning method for estimating food volume from a single depth image. This approach synthesizes novel views, enabling accurate 3D reconstruction and dietary intake assessment.

Keywords:
3d reconstructiondeep learningdietary assessmentimage renderingmhealthview synthesisvolume estimation

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Nutrition Science

Background:

  • Accurate dietary assessment requires precise food volume measurement.
  • Existing methods for volume estimation often involve manual steps or multiple image captures, limiting user convenience.

Purpose of the Study:

  • To develop a novel deep learning-based view synthesis approach for 3D food reconstruction.
  • To estimate food volume accurately from a single depth image, simplifying dietary assessment.

Main Methods:

  • A deep neural network was designed to synthesize depth images from novel viewpoints.
  • 3D point clouds were reconstructed by fusing original and synthesized data using point cloud completion and Iterative Closest Point (ICP) algorithms.
  • A database of food depth images was created for validation.

Main Results:

  • The proposed method successfully reconstructed 3D point clouds of food items.
  • Volume estimation using synthesized 3D point clouds showed accuracy when compared to ground truth.

Conclusions:

  • The view synthesis approach offers a promising, user-friendly method for objective dietary assessment.
  • This technology can enhance understanding of dietary behavior and support targeted health interventions.