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dc.contributor.authorMa, Qianen_US
dc.contributor.authorWei, Guangshunen_US
dc.contributor.authorZhou, Yuanfengen_US
dc.contributor.authorPan, Xiaoen_US
dc.contributor.authorXin, Shiqingen_US
dc.contributor.authorWang, Wenpingen_US
dc.contributor.editorEisemann, Elmar and Jacobson, Alec and Zhang, Fang-Lueen_US
dc.description.abstract3D scanned point cloud data of teeth is popular used in digital orthodontics. The classification and semantic labelling for point cloud of each tooth is a key and challenging task for planning dental treatment. Utilizing the priori ordered position information of tooth arrangement, we propose an effective network for tooth model classification in this paper. The relative position and the adjacency similarity feature vectors are calculated for tooth 3D model, and combine the geometric feature into the fully connected layers of the classification training task. For the classification of dental anomalies, we present a dental anomalies processing method to improve the classification accuracy. We also use FocalLoss as the loss function to solve the sample imbalance of wisdom teeth. The extensive evaluations, ablation studies and comparisons demonstrate that the proposed network can classify tooth models accurately and automatically and outperforms state-of-the-art point cloud classification methods.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectComputing methodologies
dc.subjectShape analysis
dc.titleSRF-Net: Spatial Relationship Feature Network for Tooth Point Cloud Classificationen_US
dc.description.seriesinformationComputer Graphics Forum

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  • 39-Issue 7
    Pacific Graphics 2020 - Symposium Proceedings

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