{"type":"ScholarlyArticle","title":"MAT-Net: Medial Axis Transform Network for 3D Object Recognition","authors":[{"name":"Jianwei Hu","slug":"jianwei-hu","given":"Jianwei","family":"Hu","affiliation":""},{"name":"Bin Wang","slug":"bin-wang","given":"Bin","family":"Wang","affiliation":""},{"name":"Lihui Qian","slug":"lihui-qian","given":"Lihui","family":"Qian","affiliation":""},{"name":"Yiling Pan","slug":"yiling-pan","given":"Yiling","family":"Pan","affiliation":""},{"name":"Xiaohu Guo","slug":"xiaohu-guo","given":"Xiaohu","family":"Guo","affiliation":""},{"name":"Lingjie Liu","slug":"lingjie-liu","given":"Lingjie","family":"Liu","affiliation":""},{"name":"Wenping Wang","slug":"wenping-wang","given":"Wenping","family":"Wang","affiliation":""}],"abstract":"3D deep learning performance depends on object representation and local feature extraction. In this work, we present MAT-Net, a neural network which captures local and global features from the Medial Axis Transform (MAT). Different from K-Nearest-Neighbor method which extracts local features by a fixed number of neighbors, our MAT-Net exploits effective modules Group-MAT and Edge-Net to process topological structure. Experimental results illustrate that MAT-Net demonstrates competitive or better performance on 3D shape recognition than state-of-the-art methods, and prove that MAT representation has excellent capacity in 3D deep learning, even in the case of low resolution.","doi":"10.24963/ijcai.2019/109","url":"https://ijcai.org/proceedings/2019/109","pdf":"https://ijcai.org/proceedings/2019/0109.pdf","markdown":"https://ijcai.org/proceedings/2019/109.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/109","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Computer Vision","track":"Main track","number":109,"pages":{"first":774,"last":781},"keywords":["Computer Vision: Computer Vision","Computer Vision: 2D and 3D Computer Vision","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}