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Fast, Accurate and Memory-Efficient Partial Permutation Synchronization

CVPR 2022 · Learning theory and algorithms › Matrix and tensor methods · approach: Randomized sketching for matrices · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use MatchFAME instead of MatchEIGUCM synchronization · UCM, n=20, m=2000runtime (seconds): 1297 -> 27Helped
Use MatchFAME instead of MatchEIGUCM synchronization · UCM, m=20, n=700runtime (seconds): >5000 -> 826Helped
Use MatchFAME instead of the strongest listed baselinePartial permutation synchronization · EPFL datasets, Herz-Jesu-P8precision (PR, %): 95.9 -> 95.9No clear effect
Use MatchFAME instead of the strongest listed baselinePartial permutation synchronization · EPFL datasets, Castle-P30precision (PR, %): 84.7 -> 87.9Helped
Use MatchFAME instead of LUD aloneLUD camera-pose pipeline · Photo Tourism, Piazza Del Popolomean rotation error (degrees): 7.04 -> 1.39Helped
Use MatchFAME instead of LUD aloneLUD camera-pose pipeline · Photo Tourism, Gendarmenmarktmean rotation error (degrees): 40.14 -> 40.20Hurt
Add cycle-inconsistency-based corruption estimation with CEMP-PartialMatchFAME · LBC and LAC synthetic corruptionprecisionHelped
Add edge confidence weights to projected power iterationsMatchFAME · Nonuniform-corruption synthetic dataprecisionHelped
Initialize projected power iterations with an MST rather than a spectral methodMatchFAME · Partial permutation synchronizationmemory efficiencyHelped
Replace dense spectral operations with sparse matrix operationsMatchFAME · UCM runtime benchmarksruntime (seconds): 1297 -> 27Helped

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