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Communication Dans Un Congrès Année : 2020

Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions


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 $10\times$, 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.
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Dates et versions

hal-02950617 , version 1 (28-09-2020)



Ignacio Rocco, Relja Arandjelovic, Josef Sivic. Efficient Neighbourhood Consensus Networks via Submanifold Sparse Convolutions. ECCV 2020 - 16th European Conference on Computer Vision, Aug 2020, Glasgow / Virtual, United Kingdom. ⟨hal-02950617⟩
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