{"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/deep3d-fully-automatic-2d-to-3d-video","title":"Deep3D: Fully Automatic 2D-to-3D Video Conversion with Deep Convolutional Neural Networks","arxiv_id":"1604.03650","date":"2016-04-13","proceeding":null,"authors":["Junyuan Xie","Ross Girshick","Ali Farhadi"],"abstract":"As 3D movie viewing becomes mainstream and Virtual Reality (VR) market\nemerges, the demand for 3D contents is growing rapidly. Producing 3D videos,\nhowever, remains challenging. In this paper we propose to use deep neural\nnetworks for automatically converting 2D videos and images to stereoscopic 3D\nformat. In contrast to previous automatic 2D-to-3D conversion algorithms, which\nhave separate stages and need ground truth depth map as supervision, our\napproach is trained end-to-end directly on stereo pairs extracted from 3D\nmovies. This novel training scheme makes it possible to exploit orders of\nmagnitude more data and significantly increases performance. Indeed, Deep3D\noutperforms baselines in both quantitative and human subject evaluations.","url_abs":"http://arxiv.org/abs/1604.03650v1","url_pdf":"http://arxiv.org/pdf/1604.03650v1.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":"deep3d-fully-automatic-2d-to-3d-video","repo_url":"https://github.com/piiswrong/deep3d","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"mxnet","reach":{"status":"ok"}},{"paper_slug":"deep3d-fully-automatic-2d-to-3d-video","repo_url":"https://github.com/Candice-X/w-net-for-image-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep3d-fully-automatic-2d-to-3d-video","repo_url":"https://github.com/LouisFoucard/w-net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep3d-fully-automatic-2d-to-3d-video","repo_url":"https://github.com/pesuchin/Deep3D-chainer","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.03650","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}