Hpatches: A Benchmark And Evaluation Of Handcrafted And Learned Local Descriptors

Abstract

In this paper, we propose a novel benchmark for evaluating local image descriptors. We demonstrate that the existing datasets and evaluation protocols do not specify unambiguously all aspects of evaluation, leading to ambiguities and inconsistencies in results reported in the literature. Furthermore, these datasets are nearly saturated due to the recent improvements in local descriptors obtained by learning them from large annotated datasets. Therefore, we introduce a new large dataset suitable for training and testing modern descriptors, together with strictly defined evaluation protocols in several tasks such as matching, retrieval and classification. This allows for more realistic, and thus more reliable comparisons in different application scenarios. We evaluate the performance of several state-of-the-art descriptors and analyse their properties. We show that a simple normalisation of traditional hand-crafted descriptors can boost their performance to the level of deep learning bas

Stats

• citations29
• S2 citations—
• github stars0
• HF likes0
• heat score11.08
• arxiv keybalntas2017hpatches

Related papers

• A Large Dataset For Improving Patch Matching (2018)0.00
• Learning And Aggregating Deep Local Descriptors For Instance-level Recognition (2020)13.88
• Learning Local Descriptors By Optimizing The Keypoint-correspondence Criterion: Applications To Face Matching, Learning From Unlabeled Videos And 3d-shape Retrieval (2016)11.75
• Pn-net: Conjoined Triple Deep Network For Learning Local Image Descriptors (2016)0.00
• Hynet: Learning Local Descriptor With Hybrid Similarity Measure And Triplet Loss (2020)0.00
• Working Hard To Know Your Neighbor's Margins: Local Descriptor Learning Loss (2017)0.00
• Learning Geodesic-aware Local Features From RGB-D Images (2022)6.34
• Digging Into Self-supervised Learning Of Feature Descriptors (2021)7.16