{"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/dual-level-adaptive-self-labeling-for-novel","title":"Dual-level Adaptive Self-Labeling for Novel Class Discovery in Point Cloud Segmentation","arxiv_id":"2407.12489","date":"2024-07-17","proceeding":null,"authors":["Ruijie Xu","Chuyu Zhang","Hui Ren","Xuming He"],"abstract":"We tackle the novel class discovery in point cloud segmentation, which discovers novel classes based on the semantic knowledge of seen classes. Existing work proposes an online point-wise clustering method with a simplified equal class-size constraint on the novel classes to avoid degenerate solutions. However, the inherent imbalanced distribution of novel classes in point clouds typically violates the equal class-size constraint. Moreover, point-wise clustering ignores the rich spatial context information of objects, which results in less expressive representation for semantic segmentation. To address the above challenges, we propose a novel self-labeling strategy that adaptively generates high-quality pseudo-labels for imbalanced classes during model training. In addition, we develop a dual-level representation that incorporates regional consistency into the point-level classifier learning, reducing noise in generated segmentation. Finally, we conduct extensive experiments on two widely used datasets, SemanticKITTI and SemanticPOSS, and the results show our method outperforms the state of the art by a large margin.","url_abs":"https://arxiv.org/abs/2407.12489v1","url_pdf":"https://arxiv.org/pdf/2407.12489v1.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":"dual-level-adaptive-self-labeling-for-novel","repo_url":"https://github.com/RikkiXu/NCD_PC","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"novel-class-discovery","task_name":"Novel Class Discovery"},{"task_slug":"point-cloud-segmentation","task_name":"Point Cloud Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2407.12489","atlas_url":"https://app.syntology.ai/?focus=2407.12489","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.12489"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/RikkiXu/NCD_PC","reach":{"status":"ok"}}],"summary":{"ran":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"bac5bf24a05a11a1","entry":"calculate_ASA","repo":"RikkiXu/NCD_PC","repo_kind":"official","path":"modules/Discover.py","file_url":"https://github.com/RikkiXu/NCD_PC/blob/HEAD/modules/Discover.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bac5bf24a05a11a1"}},{"code_sha256_prefix":"1cfda515a69f8682","entry":"initialize_scheduler","repo":"RikkiXu/NCD_PC","repo_kind":"official","path":"utils/lr.py","file_url":"https://github.com/RikkiXu/NCD_PC/blob/HEAD/utils/lr.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"1cfda515a69f8682"}},{"code_sha256_prefix":"753421a31902aa14","entry":"split_tensor_by_list","repo":"RikkiXu/NCD_PC","repo_kind":"official","path":"modules/Discover.py","file_url":"https://github.com/RikkiXu/NCD_PC/blob/HEAD/modules/Discover.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"753421a31902aa14"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}