Patent ID: 11954899
Assignee: GOOGLE LLC
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 16:
17. A processing system comprising:
a memory storing a neural network; and
one or more processors coupled to the memory and configured to use the neural network to predict correspondences in images,
wherein the neural network has been trained to predict correspondences in images pursuant to a training method comprising:
generating a first feature map based on a first image of a subject, and a second feature map based on a second image of the subject, the first image and the second image being different and having been generated using a three-dimensional model of the subject;
determining a first feature distance between a first point as represented in the first feature map and a second point as represented in the second feature map, the first point and the second point corresponding to the same feature on the three-dimensional model of the subject;
determining a second feature distance between a third point as represented in the first feature map and a fourth point as represented in the first feature map;
determining a first geodesic distance between the third point and the fourth point as represented in a first surface map, the first surface map corresponding to the first image and having been generated using the three-dimensional model of the subject;
determining a third feature distance between the third point as represented in the first feature map and a fifth point as represented in the first feature map;
determining a second geodesic distance between the third point and the fifth point as represented in the first surface map;
determining a first loss value of a set of loss values, the first loss value being based on the first feature distance;
determining a second loss value of the set of loss values, the second loss value being based on the second feature distance, the third feature distance, the first geodesic distance, and the second geodesic distance; and
modifying one or more parameters of the neural network based at least in part on the set of loss values.