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In this work we target the problem of estimating accurately localisedcorrespondences between a pair of images. We adopt the recent NeighbourhoodConsensus Networks that have demonstrated promising performance for difficultcorrespondence problems and propose modifications to overcome their mainlimitations: large memory consumption, large inference time and poorlylocalised correspondences. Our proposed modifications can reduce the memoryfootprint and execution time more than $10\times$, with equivalent results.This is achieved by sparsifying the correlation tensor containing tentativematches, and its subsequent processing with a 4D CNN using submanifold sparseconvolutions. Localisation accuracy is significantly improved by processing theinput images in higher resolution, which is possible due to the reduced memoryfootprint, and by a novel two-stage correspondence relocalisation module. Theproposed Sparse-NCNet method obtains state-of-the-art results on the HPatchesSequences and InLoc visual localisation benchmarks, and competitive results inthe Aachen Day-Night benchmark.

Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions