{"type":"ScholarlyArticle","title":"A Comprehensive Survey and Taxonomy on Point Cloud Registration Based on Deep Learning","authors":[{"name":"Yu-Xin Zhang","slug":"yu-xin-zhang","given":"Yu-Xin","family":"Zhang","affiliation":"Southeast University"},{"name":"Jie Gui","slug":"jie-gui","given":"Jie","family":"Gui","affiliation":"Southeast University; Engineering Research Center of Blockchain Application , Supervision And Management (Southeast University) , Ministry of Education; Purple Mountain Laboratories"},{"name":"Xiaofeng Cong","slug":"xiaofeng-cong","given":"Xiaofeng","family":"Cong","affiliation":"Southeast University"},{"name":"Xin Gong","slug":"xin-gong","given":"Xin","family":"Gong","affiliation":"Southeast University"},{"name":"Wenbing Tao","slug":"wenbing-tao","given":"Wenbing","family":"Tao","affiliation":"Huazhong University of Science and Technology"}],"abstract":"Point cloud registration (PCR) involves determining a rigid transformation that aligns one point cloud to another. Despite the plethora of outstanding deep learning (DL)-based registration methods proposed, comprehensive and systematic studies on DL-based PCR techniques are still lacking. In this paper, we present a comprehensive survey and taxonomy of recently proposed PCR methods. Firstly, we conduct a taxonomy of commonly utilized datasets and evaluation metrics. Secondly, we classify the existing research into two main categories: supervised and unsupervised registration, providing insights into the core concepts of various influential PCR models. Finally, we highlight open challenges and potential directions for future research. A curated collection of valuable resources is made available at https://github.com/yxzhang15/PCR.","doi":"10.24963/ijcai.2024/922","url":"https://ijcai.org/proceedings/2024/922","pdf":"https://ijcai.org/proceedings/2024/0922.pdf","markdown":"https://ijcai.org/proceedings/2024/922.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/922","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Survey Track","track":"Survey Track","number":922,"pages":{"first":8344,"last":8353},"keywords":["Computer Vision: General","Computer Vision: CV: 3D computer vision"],"videos":[],"references":[{"position":1,"raw":"[Ao et al., 2021] S. 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