{"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/spidercnn-deep-learning-on-point-sets-with","title":"SpiderCNN: Deep Learning on Point Sets with Parameterized Convolutional Filters","arxiv_id":"1803.11527","date":"2018-03-30","proceeding":"ECCV 2018 9","authors":["Yifan Xu","Tianqi Fan","Mingye Xu","Long Zeng","Yu Qiao"],"abstract":"Deep neural networks have enjoyed remarkable success for various vision\ntasks, however it remains challenging to apply CNNs to domains lacking a\nregular underlying structures such as 3D point clouds. Towards this we propose\na novel convolutional architecture, termed SpiderCNN, to efficiently extract\ngeometric features from point clouds. SpiderCNN is comprised of units called\nSpiderConv, which extend convolutional operations from regular grids to\nirregular point sets that can be embedded in R^n, by parametrizing a family of\nconvolutional filters. We design the filter as a product of a simple step\nfunction that captures local geodesic information and a Taylor polynomial that\nensures the expressiveness. SpiderCNN inherits the multi-scale hierarchical\narchitecture from classical CNNs, which allows it to extract semantic deep\nfeatures. Experiments on ModelNet40 demonstrate that SpiderCNN achieves\nstate-of-the-art accuracy 92.4% on standard benchmarks, and shows competitive\nperformance on segmentation task.","url_abs":"http://arxiv.org/abs/1803.11527v3","url_pdf":"http://arxiv.org/pdf/1803.11527v3.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":"spidercnn-deep-learning-on-point-sets-with","repo_url":"https://github.com/xyf513/SpiderCNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-part-segmentation-on-intra","task":"3D Part Segmentation","dataset":"IntrA","model":"SpiderCNN","rank_in_archive_order":6,"of":7,"metrics":{"DSC (A)":"75.82","DSC (V)":"94.53","IoU (A)":"67.25","IoU (V)":"90.16"},"uses_additional_data":false},{"leaderboard":"/sota/3d-part-segmentation-on-shapenet-part","task":"3D Part Segmentation","dataset":"ShapeNet-Part","model":"SpiderCNN","rank_in_archive_order":49,"of":67,"metrics":{"Class Average IoU":"82.4","Instance Average IoU":"85.3"},"uses_additional_data":false},{"leaderboard":"/sota/3d-point-cloud-classification-on-intra","task":"3D Point Cloud Classification","dataset":"IntrA","model":"SpiderCNN","rank_in_archive_order":7,"of":12,"metrics":{"F1 score (5-fold)":"0.872"},"uses_additional_data":false},{"leaderboard":"/sota/3d-point-cloud-classification-on-modelnet40","task":"3D Point Cloud Classification","dataset":"ModelNet40","model":"SpiderCNN","rank_in_archive_order":89,"of":111,"metrics":{"Overall Accuracy":"92.4"},"uses_additional_data":false},{"leaderboard":"/sota/3d-point-cloud-classification-on-scanobjectnn","task":"3D Point Cloud Classification","dataset":"ScanObjectNN","model":"SpiderCNN","rank_in_archive_order":75,"of":77,"metrics":{"Mean Accuracy":"69.8","Overall Accuracy":"73.7"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.11527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.11527"}},"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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