Object pose neural network system

A computer-implemented method comprising:

• receiving an image, wherein the image is of an object;
• providing the image as an input to a neural network system trained to generate an output comprising a plurality of heat maps corresponding to a plurality of feature points, each heat map representing a likelihood, for each image region of a plurality of image regions in the image, that the image region corresponds to a respective one of the plurality of feature points;
• applying a differentiable transformation on each of the plurality of heat maps to generate a plurality of feature coordinates for the plurality of feature points;
• providing the plurality of feature coordinates for the plurality of feature points;
• obtaining a ground truth pose for the object by:
• obtaining respective ground truth object poses of the object based on pose information contained in each of one or more other images;
• computing one or more respective relative displacements of the object using kinematic data associated with the image and respective kinematic data associated with each of the one or more other images;
• and
• computing an interpolated ground truth object pose of the object based on a respective ground truth pose of the object in each of the one or more other images and the one or more respective relative displacements;
• inputting the plurality of feature coordinates for the plurality of feature points into an object pose solver configured to compute a predicted object pose for the object, wherein the predicted object pose for the object specifies a position and an orientation of the object;
• and
• outputting the predicted object pose for the object.