{"type":"ScholarlyArticle","title":"MM-PCQA: Multi-Modal Learning for No-reference Point Cloud Quality Assessment","authors":[{"name":"Zicheng Zhang","slug":"zicheng-zhang","given":"Zicheng","family":"Zhang","affiliation":"Institute of Image Communication and Network Engineering , Shanghai Jiao Tong University"},{"name":"Wei Sun","slug":"wei-sun","given":"Wei","family":"Sun","affiliation":"Institute of Image Communication and Network Engineering , Shanghai Jiao Tong University"},{"name":"Xiongkuo Min","slug":"xiongkuo-min","given":"Xiongkuo","family":"Min","affiliation":"Institute of Image Communication and Network Engineering , Shanghai Jiao Tong University"},{"name":"Qiyuan Wang","slug":"qiyuan-wang","given":"Qiyuan","family":"Wang","affiliation":"Bilibili Inc , Shanghai , China"},{"name":"Jun He","slug":"jun-he","given":"Jun","family":"He","affiliation":"Bilibili Inc , Shanghai , China"},{"name":"Quan Zhou","slug":"quan-zhou","given":"Quan","family":"Zhou","affiliation":"Bilibili Inc , Shanghai , China"},{"name":"Guangtao Zhai","slug":"guangtao-zhai","given":"Guangtao","family":"Zhai","affiliation":"Institute of Image Communication and Network Engineering , Shanghai Jiao Tong University; MoE Key Lab of Artificial Intelligence , AI Institute , Shanghai Jiao Tong University"}],"abstract":"The visual quality of point clouds has been greatly emphasized since the ever-increasing 3D vision applications are expected to provide cost-effective and high-quality experiences for users.  Looking back on the development of point cloud quality assessment (PCQA), the visual quality is usually evaluated by utilizing single-modal information, i.e., either extracted from the 2D projections or 3D point cloud. The 2D projections contain rich texture and semantic information but are highly dependent on viewpoints, while the 3D point clouds are more sensitive to geometry distortions and invariant to viewpoints. Therefore, to leverage the advantages of both point cloud and projected image modalities, we propose a novel no-reference Multi-Modal Point Cloud Quality Assessment (MM-PCQA) metric. In specific, we split the point clouds into sub-models to represent local geometry distortions such as point shift and down-sampling. Then we render the point clouds into 2D image projections for texture feature extraction. To achieve the goals, the sub-models and projected images are encoded with point-based and image-based neural networks. Finally, symmetric cross-modal attention is employed to fuse multi-modal quality-aware information. Experimental results show that our approach outperforms all compared state-of-the-art methods and is far ahead of previous no-reference PCQA methods, which highlights the effectiveness of the proposed method. The code is available at https://github.com/zzc-1998/MM-PCQA.","doi":"10.24963/ijcai.2023/195","url":"https://ijcai.org/proceedings/2023/195","pdf":"https://ijcai.org/proceedings/2023/0195.pdf","markdown":"https://ijcai.org/proceedings/2023/195.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/195","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":195,"pages":{"first":1759,"last":1767},"keywords":["Computer Vision: CV: 3D computer vision","Machine Learning: ML: Multi-modal learning"],"videos":[],"references":[{"position":1,"raw":"[Afham et al., 2022] Mohamed Afham, Isuru Dissanayake, Dinithi Dissanayake, Amaya Dharmasiri, Kanchana Thi- lakarathna, and Ranga Rodrigo. 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