  This paper focuses on pose registration of different object instances from
the same category. This is required in online object mapping because object
instances detected at test time usually differ from the training instances. Our
approach transforms instances of the same category to a normalized canonical
coordinate frame and uses metric learning to train fully convolutional
geometric features. The resulting model is able to generate pairs of matching
points between the instances, allowing category-level registration. Evaluation
on both synthetic and real-world data shows that our method provides robust
features, leading to accurate alignment of instances with different shapes.
