{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hierarchical-encoding-of-sequential-data-with","title":"Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost","arxiv_id":null,"date":"2019-10-01","proceeding":"ICCV 2019 10","authors":["Huu Le"," Ming Xu"," Tuan Hoang"," Michael Milford"],"abstract":" Snapshot-based visual localization is an important problem in several computer vision and robotics applications such as Simultaneous Localization And Mapping (SLAM). To achieve real-time performance in very large-scale environments with massive amounts of training and map data, techniques such as approximate nearest neighbor search (ANN) algorithms are used. While several state-of-the-art variants of quantization and indexing techniques have demonstrated to be efficient in practice, their theoretical memory cost still scales at least linearly with the training data (i.e., O(n) where n is the number of instances in the database), since each data point must be associated with at least one code vector. To address these limitations, in this paper we present a totally new hierarchical encoding approach that enables a sub-linear storage scale. The algorithm exploits the widespread sequential nature of sensor information streams in robotics and autonomous vehicle applications and achieves, both theoretically and experimentally, sub-linear scalability in storage required for a given environment size. Furthermore, the associated query time of our algorithm is also of sub-linear complexity. We benchmark the performance of the proposed algorithm on several real-world benchmark datasets and experimentally validate the theoretical sub-linearity of our approach, while also showing that our approach yields competitive absolute storage performance as well. \r","url_abs":"http://openaccess.thecvf.com/content_ICCV_2019/html/Le_Hierarchical_Encoding_of_Sequential_Data_With_Compact_and_Sub-Linear_Storage_ICCV_2019_paper.html","url_pdf":"http://openaccess.thecvf.com/content_ICCV_2019/papers/Le_Hierarchical_Encoding_of_Sequential_Data_With_Compact_and_Sub-Linear_Storage_ICCV_2019_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hierarchical-encoding-of-sequential-data-with","repo_url":"https://github.com/intellhave/HESSL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"simultaneous-localization-and-mapping","task_name":"Simultaneous Localization and Mapping"},{"task_slug":"visual-localization","task_name":"Visual Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}