{"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/pointgrid-a-deep-network-for-3d-shape","title":"PointGrid: A Deep Network for 3D Shape Understanding","arxiv_id":null,"date":"2018-06-01","proceeding":"CVPR 2018 6","authors":["Truc Le","Ye Duan"],"abstract":"This paper presents a new deep learning architecture called PointGrid that is designed for 3D model recognition from unorganized point clouds. The new architecture embeds the input point cloud into a 3D grid by a simple, yet effective, sampling strategy and directly learns transformations and features from their raw coordinates. The proposed method is an integration of point and grid, a hybrid model, that leverages the simplicity of grid-based approaches such as VoxelNet while avoid its information loss. PointGrid learns better global information compared with PointNet and is much simpler than PointNet++, Kd-Net, Oct-Net and O-CNN, yet provides comparable recognition accuracy. With experiments on popular shape recognition benchmarks, PointGrid demonstrates competitive performance over existing deep learning methods on both classification and segmentation.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2018/html/Le_PointGrid_A_Deep_CVPR_2018_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2018/papers/Le_PointGrid_A_Deep_CVPR_2018_paper.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":"pointgrid-a-deep-network-for-3d-shape","repo_url":"https://github.com/trucleduc/PointGrid","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","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":"deep-learning","task_name":"Deep Learning"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-part-segmentation-on-shapenet-part","task":"3D Part Segmentation","dataset":"ShapeNet-Part","model":"PointGrid","rank_in_archive_order":28,"of":67,"metrics":{"Class Average IoU":"82.2","Instance Average IoU":"86.4"},"uses_additional_data":false},{"leaderboard":"/sota/3d-point-cloud-classification-on-modelnet40","task":"3D Point Cloud Classification","dataset":"ModelNet40","model":"PointGrid","rank_in_archive_order":95,"of":111,"metrics":{"Overall Accuracy":"92.0"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}