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Thus far, however, point features have been abstracted in an\nindependent and isolated manner, ignoring the relative layout of neighboring\npoints as well as their features. In the present article, we propose to\novercome this limitation by using spectral graph convolution on a local graph,\ncombined with a novel graph pooling strategy. In our approach, graph\nconvolution is carried out on a nearest neighbor graph constructed from a\npoint's neighborhood, such that features are jointly learned. We replace the\nstandard max pooling step with a recursive clustering and pooling strategy,\ndevised to aggregate information from within clusters of nodes that are close\nto one another in their spectral coordinates, leading to richer overall feature\ndescriptors. Through extensive experiments on diverse datasets, we show a\nconsistent demonstrable advantage for the tasks of both point set\nclassification and segmentation.","url_abs":"http://arxiv.org/abs/1803.05827v1","url_pdf":"http://arxiv.org/pdf/1803.05827v1.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":"local-spectral-graph-convolution-for-point","repo_url":"https://github.com/fate3439/LocalSpecGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-point-cloud-classification","task_name":"3D Point Cloud Classification"},{"task_slug":"clustering","task_name":"Clustering"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-point-cloud-classification-on-modelnet40","task":"3D Point Cloud Classification","dataset":"ModelNet40","model":"SpecGCN","rank_in_archive_order":93,"of":111,"metrics":{"Overall Accuracy":"92.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.05827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1803.05827"}},"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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