{"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/octnet-learning-deep-3d-representations-at","title":"OctNet: Learning Deep 3D Representations at High Resolutions","arxiv_id":"1611.05009","date":"2016-11-15","proceeding":"CVPR 2017 7","authors":["Gernot Riegler","Ali Osman Ulusoy","Andreas Geiger"],"abstract":"We present OctNet, a representation for deep learning with sparse 3D data. In\ncontrast to existing models, our representation enables 3D convolutional\nnetworks which are both deep and high resolution. Towards this goal, we exploit\nthe sparsity in the input data to hierarchically partition the space using a\nset of unbalanced octrees where each leaf node stores a pooled feature\nrepresentation. This allows to focus memory allocation and computation to the\nrelevant dense regions and enables deeper networks without compromising\nresolution. We demonstrate the utility of our OctNet representation by\nanalyzing the impact of resolution on several 3D tasks including 3D object\nclassification, orientation estimation and point cloud labeling.","url_abs":"http://arxiv.org/abs/1611.05009v4","url_pdf":"http://arxiv.org/pdf/1611.05009v4.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":"octnet-learning-deep-3d-representations-at","repo_url":"https://github.com/griegler/octnet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"torch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"3d-object-classification","task_name":"3D Object Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1611.05009","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}