{"doi":"10.1007/s44267-024-00046-x","title":"A review of point cloud segmentation for understanding 3D indoor scenes","abstract":"<jats:title>Abstract</jats:title><jats:p>Point cloud segmentation is an essential task in three-dimensional (3D) vision and intelligence. It is a critical step in understanding 3D scenes with a variety of applications. With the rapid development of 3D scanning devices, point cloud data have become increasingly available to researchers. Recent advances in deep learning are driving advances in point cloud segmentation research and applications. This paper presents a comprehensive review of recent progress in point cloud segmentation for understanding 3D indoor scenes. First, we present public point cloud datasets, which are the foundation for research in this area. Second, we briefly review previous segmentation methods based on geometry. Then, learning-based segmentation methods with multi-views and voxels are presented. Next, we provide an overview of learning-based point cloud segmentation, ranging from semantic segmentation to instance segmentation. Based on the annotation level, these methods are categorized into fully supervised and weakly supervised methods. Finally, we discuss open challenges and research directions in the future.</jats:p>","journal":"Visual Intelligence","year":2024,"id":38206,"datarank":0.7243407055116022,"base_score":3.4011973816621555,"endowment":3.4011973816621555,"self_citation_contribution":0.5101796072493234,"citation_network_contribution":0.21416109826227875,"self_endowment_contribution":0.5101796072493234,"citer_contribution":0.21416109826227875,"corpus_percentile":null,"corpus_rank":null,"citation_count":29,"citer_count":28,"citers_with_citation_signal":10,"citers_with_endowment":10,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":189902,"name":"Xudong Zhang","orcid":"0000-0002-9666-405X","position":1,"is_corresponding":false},{"id":189903,"name":"Yongwei Miao","orcid":"0000-0002-5479-9060","position":2,"is_corresponding":false},{"id":189901,"name":"Yuliang Sun","orcid":"0009-0000-1428-0857","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"base_score":3.367295829986474,"endowment":3.367295829986474,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"18998881","pmcid":null,"openalex_id":"https://openalex.org/W4399442168","authors":[],"funders":[{"funder_name":"National Natural Science Foundation of China","grant_id":"61972458","title":null},{"funder_name":"Zhejiang Provincial Natural Science Foundation of China","grant_id":"LZ23F020002","title":null}],"total_grants":2,"fwci":9.2736,"citation_percentile":0.9913166,"influential_citations":0,"citation_trend":[{"year":2024,"count":2},{"year":2025,"count":13},{"year":2026,"count":13}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://link.springer.com/content/pdf/10.1007/s44267-024-00046-x.pdf","host_type":"journal"},{"url":"https://link.springer.com/content/pdf/10.1007/s44267-024-00046-x.pdf","host_type":"GOLD"},{"url":"https://link.springer.com/content/pdf/10.1007/s44267-024-00046-x.pdf","host_type":"publisher"},{"url":"https://link.springer.com/article/10.1007/s44267-024-00046-x/fulltext.html","host_type":"publisher"},{"url":"https://doi.org/10.1007/s44267-024-00046-x","host_type":"journal"}],"fields_of_study":["3D Shape Modeling and Analysis","3D Surveying and Cultural Heritage","Remote Sensing and LiDAR Applications","Computer Science","Engineering"],"mesh_terms":[],"keywords":["Point cloud","Segmentation","Computer science","Artificial intelligence","Point (geometry)","Image segmentation","Deep learning","Cloud computing","Scale-space segmentation","Machine learning","Computer vision","Data science","Mathematics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-06-11T01:29:30.206664Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}