Systems and methods for training models to predict dense correspondences in images using geodesic distances

A processing system comprising:

• memory storing a neural network;
• and
• one or more processors operatively 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:
• determining a first feature distance between a first point as represented in a first feature map and a second point as represented in a second feature map, the first point and the second point corresponding to the same feature on a three-dimensional model of a subject, the first feature map being based on a first image of the subject, and the second feature map being based on a second image of the subject;
• determining a first geodesic distance between a first pair of selected points as represented in a surface map corresponding to the first image;
• determining a second geodesic distance between a second pair of selected points as represented in the surface map;
• and
• modifying one or more parameters of the neural network based at least in part on a pair of loss values, a first one of the pair of loss values being based on the first feature distance, and a second one of the pair of loss values being based on at least the first and second geodesic distances.