{"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/sharingan-combining-synthetic-and-real-data-1","title":"SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation","arxiv_id":"2006.04026","date":"2020-06-07","proceeding":"CVPR 2020 6","authors":["Koutilya PNVR","Hao Zhou","David Jacobs"],"abstract":"We propose a novel method for combining synthetic and real images when training networks to determine geometric information from a single image. We suggest a method for mapping both image types into a single, shared domain. This is connected to a primary network for end-to-end training. Ideally, this results in images from two domains that present shared information to the primary network. Our experiments demonstrate significant improvements over the state-of-the-art in two important domains, surface normal estimation of human faces and monocular depth estimation for outdoor scenes, both in an unsupervised setting.","url_abs":"https://arxiv.org/abs/2006.04026v1","url_pdf":"https://arxiv.org/pdf/2006.04026v1.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":"sharingan-combining-synthetic-and-real-data-1","repo_url":"https://github.com/koutilya40192/SharinGAN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"surface-normal-estimation","task_name":"Surface Normal Estimation"},{"task_slug":"surface-normals-estimation","task_name":"Surface Normals Estimation"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/monocular-depth-estimation-on-kitti-eigen-1","task":"Monocular Depth Estimation","dataset":"KITTI Eigen split unsupervised","model":"SharinGAN","rank_in_archive_order":44,"of":55,"metrics":{"Delta < 1.25":"0.864","Delta < 1.25^2":"0.954","Delta < 1.25^3":"0.981","RMSE":"3.77","RMSE log":"0.19","Sq Rel":"0.673","absolute relative error":"0.109"},"uses_additional_data":false},{"leaderboard":"/sota/monocular-depth-estimation-on-make3d","task":"Monocular Depth Estimation","dataset":"Make3D","model":"SharinGAN","rank_in_archive_order":3,"of":6,"metrics":{"Abs Rel":"0.377","RMSE":"8.388","Sq Rel":"4.9"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2006.04026","atlas_url":"https://app.syntology.ai/?focus=2006.04026","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}