Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions - ECCV 2020 (10min)

ECCV 2020

Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions - ECCV 2020 (10min)

Aug 18, 2020
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In this work we target the problem of estimating accurately localised correspondences between a pair of images. We adopt the recent Neighbourhood Consensus Networks that have demonstrated promising performance for difficult correspondence problems and propose modifications to overcome their main limitations: large memory consumption, large inference time and poorly localised correspondences. Our proposed modifications can reduce the memory footprint and execution time more than 10×, with equivalent results. This is achieved by sparsifying the correlation tensor containing tentative matches, and its subsequent processing with a 4D CNN using submanifold sparse convolutions. Localisation accuracy is significantly improved by processing the input images in higher resolution, which is possible due to the reduced memory footprint, and by a novel two-stage correspondence relocalisation module. The proposed Sparse-NCNet method obtains state-of-the-art results on the HPatches Sequences and InLoc visual localisation benchmarks, and competitive results in the Aachen Day-Night benchmark. Project website: https://www.di.ens.fr/willow/research/sparse-ncnet/ arXiv: https://arxiv.org/abs/2004.10566 Github: https://github.com/ignacio-rocco/sparse-ncnet

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