US Patent Application 17954535. METHOD FOR TRAINING DEPTH ESTIMATION MODEL, METHOD FOR ESTIMATING DEPTH, AND ELECTRONIC DEVICE simplified abstract

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METHOD FOR TRAINING DEPTH ESTIMATION MODEL, METHOD FOR ESTIMATING DEPTH, AND ELECTRONIC DEVICE

Organization Name

HON HAI PRECISION INDUSTRY CO., LTD.

Inventor(s)

YU-HSUAN Chien of New Taipei (TW)

CHIN-PIN Kuo of New Taipei (TW)

METHOD FOR TRAINING DEPTH ESTIMATION MODEL, METHOD FOR ESTIMATING DEPTH, AND ELECTRONIC DEVICE - A simplified explanation of the abstract

This abstract first appeared for US patent application 17954535 titled 'METHOD FOR TRAINING DEPTH ESTIMATION MODEL, METHOD FOR ESTIMATING DEPTH, AND ELECTRONIC DEVICE

Simplified Explanation

The patent application describes a method for training a depth estimation model in an electronic device. Here are the key points:

  • The method involves obtaining a pair of images from a training data set.
  • The first image is inputted into the depth estimation model to obtain a disparity map.
  • The disparity map is then added to the first image to generate a second image.
  • The pixel values of corresponding pixels in the first and second images are compared using mean square error and cosine similarity calculations.
  • Mean values of the mean square error and cosine similarity are calculated.
  • The first mean value represents the average error between the pixel values of the first and second images.
  • The second mean value represents the average similarity between the pixel values of the first and second images.
  • The first and second mean values are added together to obtain a loss value for the depth estimation model.
  • The depth estimation model is iteratively trained based on the loss value, improving its accuracy over time.


Original Abstract Submitted

A method for training a depth estimation model implemented in an electronic device includes obtaining a first image pair from a training data set; inputting the first left image into the depth estimation model, and obtaining a disparity map; adding the first left image and the disparity map, and obtaining a second right image; calculating a mean square error and cosine similarity of pixel values of all corresponding pixels in the first right image and the second right image; calculating mean values of the mean square error and the cosine similarity, and obtaining a first mean value of the mean square error and a second mean value of the cosine similarity; adding the first mean value and the second mean value, and obtaining a loss value of the depth estimation model; and iteratively training the depth estimation model according to the loss value.

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