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Based on\nthe SOM, SO-Net performs hierarchical feature extraction on individual points\nand SOM nodes, and ultimately represents the input point cloud by a single\nfeature vector. The receptive field of the network can be systematically\nadjusted by conducting point-to-node k nearest neighbor search. In recognition\ntasks such as point cloud reconstruction, classification, object part\nsegmentation and shape retrieval, our proposed network demonstrates performance\nthat is similar with or better than state-of-the-art approaches. In addition,\nthe training speed is significantly faster than existing point cloud\nrecognition networks because of the parallelizability and simplicity of the\nproposed architecture. Our code is available at the project website.\nhttps://github.com/lijx10/SO-Net","url_abs":"http://arxiv.org/abs/1803.04249v4","url_pdf":"http://arxiv.org/pdf/1803.04249v4.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":"so-net-self-organizing-network-for-point","repo_url":"https://github.com/lijx10/SO-Net","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"so-net-self-organizing-network-for-point","repo_url":"https://github.com/LONG-9621/SO-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"so-net-self-organizing-network-for-point","repo_url":"https://github.com/donnyruixu/pc-elm-ae","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","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":"3d-point-cloud-linear-classification","task_name":"3D Point Cloud Linear Classification"},{"task_slug":"point-cloud-reconstruction","task_name":"Point cloud reconstruction"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"unsupervised-3d-point-cloud-linear-evaluation","task_name":"Unsupervised 3D Point Cloud Linear Evaluation"}],"methods":[{"method_slug":"som","method_name":"SOM"},{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-part-segmentation-on-intra","task":"3D Part Segmentation","dataset":"IntrA","model":"SO-Net","rank_in_archive_order":3,"of":7,"metrics":{"DSC (A)":"88.76","DSC (V)":"97.09","IoU (A)":"81.40","IoU (V)":"94.46"},"uses_additional_data":false},{"leaderboard":"/sota/3d-part-segmentation-on-shapenet-part","task":"3D Part Segmentation","dataset":"ShapeNet-Part","model":"SO-Net","rank_in_archive_order":57,"of":67,"metrics":{"Instance Average IoU":"84.9"},"uses_additional_data":false},{"leaderboard":"/sota/3d-point-cloud-classification-on-intra","task":"3D Point Cloud Classification","dataset":"IntrA","model":"SO-Net","rank_in_archive_order":9,"of":12,"metrics":{"F1 score (5-fold)":"0.868"},"uses_additional_data":false},{"leaderboard":"/sota/3d-point-cloud-classification-on-modelnet40","task":"3D Point Cloud Classification","dataset":"ModelNet40","model":"SO-Net","rank_in_archive_order":100,"of":111,"metrics":{"Overall Accuracy":"90.9"},"uses_additional_data":false},{"leaderboard":"/sota/3d-point-cloud-linear-classification-on","task":"3D Point Cloud Linear Classification","dataset":"ModelNet40","model":"SO-Net","rank_in_archive_order":19,"of":20,"metrics":{"Overall Accuracy":"87.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.04249","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.04249"}},"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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