Convolutional Hough Matching Networks

Understand

Despite advances in feature representation, leveraging geometric relations is crucial for establishing reliable visual correspondences under large variations of images.

• In this work we introduce a Hough transform perspective on convolutional matching and propose an effective geometric matching algorithm, dubbed Convolutional Hough Matching (CHM).
• The method distributes similarities of candidate matches over a geometric transformation space and evaluate them in a convolutional manner.
• We cast it into a trainable neural layer with a semi-isotropic high-dimensional kernel, which learns non-rigid matching with a small number of interpretable parameters.