{"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/3d-point-cloud-classification-and","title":"3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks","arxiv_id":"1711.08241","date":"2017-11-22","proceeding":null,"authors":["Yizhak Ben-Shabat","Michael Lindenbaum","Anath Fischer"],"abstract":"The point cloud is gaining prominence as a method for representing 3D shapes,\nbut its irregular format poses a challenge for deep learning methods. The\ncommon solution of transforming the data into a 3D voxel grid introduces its\nown challenges, mainly large memory size. In this paper we propose a novel 3D\npoint cloud representation called 3D Modified Fisher Vectors (3DmFV). Our\nrepresentation is hybrid as it combines the discrete structure of a grid with\ncontinuous generalization of Fisher vectors, in a compact and computationally\nefficient way. Using the grid enables us to design a new CNN architecture for\npoint cloud classification and part segmentation. In a series of experiments we\ndemonstrate competitive performance or even better than state-of-the-art on\nchallenging benchmark datasets.","url_abs":"http://arxiv.org/abs/1711.08241v1","url_pdf":"http://arxiv.org/pdf/1711.08241v1.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":"3d-point-cloud-classification-and","repo_url":"https://github.com/sitzikbs/3DmFV-Net","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"3d-point-cloud-classification-and","repo_url":"https://github.com/fferroni/fisher_vector_classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"3d-point-cloud-classification-and","repo_url":"https://github.com/sitzikbs/3DmFV-Net-MATLAB","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-part-segmentation","task_name":"3D Part Segmentation"},{"task_slug":"3d-point-cloud-classification","task_name":"3D Point Cloud Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"point-cloud-classification","task_name":"Point Cloud Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-part-segmentation-on-shapenet-part","task":"3D Part Segmentation","dataset":"ShapeNet-Part","model":"3DmFV-Net","rank_in_archive_order":61,"of":67,"metrics":{"Instance Average IoU":"84.3"},"uses_additional_data":false},{"leaderboard":"/sota/3d-point-cloud-classification-on-modelnet40","task":"3D Point Cloud Classification","dataset":"ModelNet40","model":"3DMFV-Net","rank_in_archive_order":98,"of":111,"metrics":{"Overall Accuracy":"91.6"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}