In this paper we present an efficient method for aggregating binary feature descriptors to allow compact representation of 3D scene model in incremental structure-from-motion and SLAM applications. All feature descriptors linked with one 3D scene point or landmark are represented by a single low-dimensional real-valued vector called a prototype. The method allows significant reduction of memory required to store and process feature descriptors in large-scale structure-from-motion applications. An efficient approximate nearest neighbours search methods suited for real-valued descriptors, such as FLANN [19], can be used on the resulting prototypes to speed up matching processed frames.
CITATION STYLE
Komorowski, J., & Trzciński, T. (2018). Aggregation of binary feature descriptors for compact scene model representation in large scale structure-from-motion applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11114 LNCS, pp. 363–374). Springer Verlag. https://doi.org/10.1007/978-3-030-00692-1_32
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