{"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/single-image-3d-interpreter-network","title":"Single Image 3D Interpreter Network","arxiv_id":"1604.08685","date":"2016-04-29","proceeding":null,"authors":["Jiajun Wu","Tianfan Xue","Joseph J. Lim","Yuandong Tian","Joshua B. Tenenbaum","Antonio Torralba","William T. Freeman"],"abstract":"Understanding 3D object structure from a single image is an important but\ndifficult task in computer vision, mostly due to the lack of 3D object\nannotations in real images. Previous work tackles this problem by either\nsolving an optimization task given 2D keypoint positions, or training on\nsynthetic data with ground truth 3D information. In this work, we propose 3D\nINterpreter Network (3D-INN), an end-to-end framework which sequentially\nestimates 2D keypoint heatmaps and 3D object structure, trained on both real\n2D-annotated images and synthetic 3D data. This is made possible mainly by two\ntechnical innovations. First, we propose a Projection Layer, which projects\nestimated 3D structure to 2D space, so that 3D-INN can be trained to predict 3D\nstructural parameters supervised by 2D annotations on real images. Second,\nheatmaps of keypoints serve as an intermediate representation connecting real\nand synthetic data, enabling 3D-INN to benefit from the variation and abundance\nof synthetic 3D objects, without suffering from the difference between the\nstatistics of real and synthesized images due to imperfect rendering. The\nnetwork achieves state-of-the-art performance on both 2D keypoint estimation\nand 3D structure recovery. We also show that the recovered 3D information can\nbe used in other vision applications, such as 3D rendering and image retrieval.","url_abs":"http://arxiv.org/abs/1604.08685v2","url_pdf":"http://arxiv.org/pdf/1604.08685v2.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":"single-image-3d-interpreter-network","repo_url":"https://github.com/mostafaramadann/Single-Image-3D-reconstruction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"image-retrieval","task_name":"Image Retrieval"},{"task_slug":"keypoint-estimation","task_name":"Keypoint Estimation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"retrieval","task_name":"Retrieval"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1604.08685","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}