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It remains, however, a challenging task, particularly for\nlarge-scale environments and in presence of significant appearance changes.\nState-of-the-art methods not only struggle with such scenarios, but are often\ntoo resource intensive for certain real-time applications. In this paper we\npropose HF-Net, a hierarchical localization approach based on a monolithic CNN\nthat simultaneously predicts local features and global descriptors for accurate\n6-DoF localization. We exploit the coarse-to-fine localization paradigm: we\nfirst perform a global retrieval to obtain location hypotheses and only later\nmatch local features within those candidate places. This hierarchical approach\nincurs significant runtime savings and makes our system suitable for real-time\noperation. By leveraging learned descriptors, our method achieves remarkable\nlocalization robustness across large variations of appearance and sets a new\nstate-of-the-art on two challenging benchmarks for large-scale localization.","url_abs":"http://arxiv.org/abs/1812.03506v2","url_pdf":"http://arxiv.org/pdf/1812.03506v2.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":"from-coarse-to-fine-robust-hierarchical","repo_url":"https://github.com/ethz-asl/hf_net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"from-coarse-to-fine-robust-hierarchical","repo_url":"https://github.com/ethz-asl/hfnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"from-coarse-to-fine-robust-hierarchical","repo_url":"https://github.com/cvg/Hierarchical-Localization","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"visual-localization","task_name":"Visual Localization"},{"task_slug":"visual-place-recognition","task_name":"Visual Place Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-place-recognition-on-berlin-kudamm","task":"Visual Place Recognition","dataset":"Berlin Kudamm","model":"HF-Net","rank_in_archive_order":3,"of":4,"metrics":{"Recall@1":"46.78"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1812.03506","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.03506"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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