The CUDA LATCH binary descriptor: Because sometimes faster means better

Christopher Parker, Matthew Daiter, Kareem Omar, Gil Levi, Tal Hassner

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Accuracy, descriptor size, and the time required for extraction and matching are all important factors when selecting local image descriptors. To optimize over all these requirements, this paper presents a CUDA port for the recent Learned Arrangement of Three Patches (LATCH) binary descriptors to the GPU platform. The design of LATCH makes it well suited for GPU processing. Owing to its small size and binary nature, the GPU can further be used to efficiently match LATCH features. Taken together, this leads to breakneck descriptor extraction and matching speeds. We evaluate the trade off between these speeds and the quality of results in a feature matching intensive application. To this end, we use our proposed CUDA LATCH (CLATCH) to recover structure from motion (SfM), comparing 3D reconstructions and speed using different representations. Our results show that CLATCH provides high quality 3D reconstructions at fractions of the time required by other representations, with little, if any, loss of reconstruction quality.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2016 Workshops, Proceedings
EditorsGang Hua, Herve Jegou
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319494081
StatePublished - 2016
EventComputer Vision - ECCV 2016 Workshops, Proceedings - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9915 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceComputer Vision - ECCV 2016 Workshops, Proceedings

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.


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