{"type":"ScholarlyArticle","title":"Rotation Invariant Spatial Networks for Single-View Point Cloud Classification","authors":[{"name":"Feng Luan","slug":"feng-luan","given":"Feng","family":"Luan","affiliation":""},{"name":"Jiarui Hu","slug":"jiarui-hu","given":"Jiarui","family":"Hu","affiliation":""},{"name":"Changshi Zhou","slug":"changshi-zhou","given":"Changshi","family":"Zhou","affiliation":""},{"name":"Zhipeng Wang","slug":"zhipeng-wang","given":"Zhipeng","family":"Wang","affiliation":""},{"name":"Jiguang Yue","slug":"jiguang-yue","given":"Jiguang","family":"Yue","affiliation":""},{"name":"Yanmin Zhou","slug":"yanmin-zhou","given":"Yanmin","family":"Zhou","affiliation":""},{"name":"Bin He","slug":"bin-he","given":"Bin","family":"He","affiliation":""}],"abstract":"Point cloud classification is critical for three-dimensional scene understanding. However, in real-world scenarios, depth cameras often capture partial, single-view point clouds of objects with different poses, making their accurate classification a challenge. In this paper, we propose a novel point cloud classification network that captures the detailed spatial structure of objects by constructing tetrahedra, which is different from point-wise operations. Specifically, we propose a RISpaNet block to extract rotation-invariant features. A rotation-invariant property generation module is designed in RISpaNet for constructing rotation-invariant tetrahedron properties (RITPs). Meanwhile, a multi-scale pooling module and a hybrid encoder are used to process RITPs to generate integrated rotation-invariant features. Further, for single-view point clouds, a complete point cloud auxiliary branch and a part-whole correlation module are jointly employed to obtain complete point cloud features from partial point clouds. Experimental results show that this network performs better than other state-of-the-art methods, evaluated on four public datasets. We achieved an overall accuracy of 94.7% (+2.0%) on ModelNet40, 93.4% (+5.9%) on MVP, 94.7% (+6.3%) on PCN and 94.8% (+1.7%) on ScanObjectNN. Our project website is https://luxurylf.github.io/RISpaNet_project/.","doi":"10.24963/ijcai.2025/189","url":"https://ijcai.org/proceedings/2025/189","pdf":"https://ijcai.org/proceedings/2025/0189.pdf","markdown":"https://ijcai.org/proceedings/2025/189.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/189","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":189,"pages":{"first":1693,"last":1701},"keywords":["Computer Vision: CV: Recognition (object detection, categorization)","Computer Vision: CV: Machine learning for vision","Computer Vision: CV: Scene analysis and understanding"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}