{"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/render-for-cnn-viewpoint-estimation-in-images","title":"Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views","arxiv_id":"1505.05641","date":"2015-05-21","proceeding":"ICCV 2015 12","authors":["Hao Su","Charles R. Qi","Yangyan Li","Leonidas Guibas"],"abstract":"Object viewpoint estimation from 2D images is an essential task in computer\nvision. However, two issues hinder its progress: scarcity of training data with\nviewpoint annotations, and a lack of powerful features. Inspired by the growing\navailability of 3D models, we propose a framework to address both issues by\ncombining render-based image synthesis and CNNs. We believe that 3D models have\nthe potential in generating a large number of images of high variation, which\ncan be well exploited by deep CNN with a high learning capacity. Towards this\ngoal, we propose a scalable and overfit-resistant image synthesis pipeline,\ntogether with a novel CNN specifically tailored for the viewpoint estimation\ntask. Experimentally, we show that the viewpoint estimation from our pipeline\ncan significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.","url_abs":"http://arxiv.org/abs/1505.05641v1","url_pdf":"http://arxiv.org/pdf/1505.05641v1.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":"render-for-cnn-viewpoint-estimation-in-images","repo_url":"https://github.com/ShapeNet/RenderForCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"render-for-cnn-viewpoint-estimation-in-images","repo_url":"https://github.com/aniketpokale10/RenderForCNN_modified","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"render-for-cnn-viewpoint-estimation-in-images","repo_url":"https://github.com/sarthaksharma13/RenderForCNN_KeypointGeneration","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"render-for-cnn-viewpoint-estimation-in-images","repo_url":"https://github.com/sarthaksharma13/RenderForCNN_annotation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"viewpoint-estimation","task_name":"Viewpoint Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1505.05641","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1505.05641"}},"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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