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We present two convolutional neural network architectures for\nHomographyNet: a regression network which directly estimates the real-valued\nhomography parameters, and a classification network which produces a\ndistribution over quantized homographies. We use a 4-point homography\nparameterization which maps the four corners from one image into the second\nimage. Our networks are trained in an end-to-end fashion using warped MS-COCO\nimages. Our approach works without the need for separate local feature\ndetection and transformation estimation stages. Our deep models are compared to\na traditional homography estimator based on ORB features and we highlight the\nscenarios where HomographyNet outperforms the traditional technique. We also\ndescribe a variety of applications powered by deep homography estimation, thus\nshowcasing the flexibility of a deep learning approach.","url_abs":"http://arxiv.org/abs/1606.03798v1","url_pdf":"http://arxiv.org/pdf/1606.03798v1.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":"deep-image-homography-estimation","repo_url":"https://github.com/DangChuong-DC/Toy-Homography","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-image-homography-estimation","repo_url":"https://github.com/JirongZhang/DeepHomography","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deep-image-homography-estimation","repo_url":"https://github.com/Phirxian/phd-airphen-alignment","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep-image-homography-estimation","repo_url":"https://github.com/fjbriones/deep-homography","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"deep-image-homography-estimation","repo_url":"https://github.com/mazenmel/Deep-homography-estimation-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"deep-image-homography-estimation","repo_url":"https://github.com/mez/deep_homography_estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deep-image-homography-estimation","repo_url":"https://github.com/richard-guinto/homographynet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"deep-image-homography-estimation","repo_url":"https://github.com/samorr/homography-net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"homography-estimation","task_name":"Homography Estimation"}],"methods":[],"datasets_introduced":[{"slug":"s-coco","name":"S-COCO","full_name":"Synthetic COCO"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/homography-estimation-on-pds-coco","task":"Homography Estimation","dataset":"PDS-COCO","model":"HomographyNet","rank_in_archive_order":3,"of":3,"metrics":{"MACE":"2.50"},"uses_additional_data":false},{"leaderboard":"/sota/homography-estimation-on-s-coco","task":"Homography Estimation","dataset":"S-COCO","model":"HomographyNet","rank_in_archive_order":3,"of":5,"metrics":{"MACE":"1.96"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.03798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.03798"}},"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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