{"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/comparative-evaluation-of-hand-crafted-and-1","title":"Comparative Evaluation of Hand-Crafted and Learned Local Features","arxiv_id":null,"date":"2017-07-01","proceeding":"Conference on Computer Vision and Pattern Recognition 2017 7","authors":["Johannes L. Sch¨onberger","Hans Hardmeier","Torsten Sattler","Marc Pollefeys"],"abstract":"Matching local image descriptors is a key step in many computer vision applications. For more than a decade,hand-crafted descriptors such as SIFT have been used for this task. Recently, multiple new descriptors learned from data have been proposed and shown to improve on SIFT interms of discriminative power. This paper is dedicated to an extensive experimental evaluation of learned local features to establish a single evaluation protocol that ensures comparable results. In terms of matching performance, we evaluate the different descriptors regarding standard criteria.However, considering matching performance in isolation only provides an incomplete measure of a descriptor’s quality. For example, finding additional correct matches between similar images does not necessarily lead to a better performance when trying to match images under extreme viewpoint or illumination changes. Besides pure descriptor matching, we thus also evaluate the different descriptors in the context of image-based reconstruction. This enables us to study the descriptor performance on a set of more practical criteria including image retrieval, the ability to register\r\nimages under strong viewpoint and illumination changes, and the accuracy and completeness of the reconstructed cameras and scenes. To facilitate future research, the full evaluation pipeline is made publicly available.","url_abs":"https://www.cvg.ethz.ch/research/local-feature-evaluation/schoenberger2017comparative.pdf","url_pdf":"https://www.cvg.ethz.ch/research/local-feature-evaluation/schoenberger2017comparative.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":"comparative-evaluation-of-hand-crafted-and-1","repo_url":"https://github.com/ahojnnes/local-feature-evaluation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[{"slug":"eth-sfm","name":"ETH SfM","full_name":"ETH Structure-from-Motion"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}