The CUDA LATCH binary descriptor: Because sometimes faster means better

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

نتاج البحث: فصل من :كتاب / تقرير / مؤتمرمنشور من مؤتمرمراجعة النظراء


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.

اللغة الأصليةالإنجليزيّة
عنوان منشور المضيفComputer Vision – ECCV 2016 Workshops, Proceedings
المحررونGang Hua, Herve Jegou
ناشرSpringer Verlag
عدد الصفحات13
رقم المعيار الدولي للكتب (المطبوع)9783319494081
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 2016
الحدثComputer Vision - ECCV 2016 Workshops, Proceedings - Amsterdam, هولندا
المدة: ٨ أكتوبر ٢٠١٦١٦ أكتوبر ٢٠١٦

سلسلة المنشورات

الاسمLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
مستوى الصوت9915 LNCS
رقم المعيار الدولي للدوريات (المطبوع)0302-9743
رقم المعيار الدولي للدوريات (الإلكتروني)1611-3349


!!ConferenceComputer Vision - ECCV 2016 Workshops, Proceedings

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Publisher Copyright:
© Springer International Publishing Switzerland 2016.


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