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The ability to segment teeth precisely from digitized 3D dental models is anessential task in computer-aided orthodontic surgical planning. To date, deeplearning based methods have been popularly used to handle this task.State-of-the-art methods directly concatenate the raw attributes of 3D inputs,namely coordinates and normal vectors of mesh cells, to train a single-streamnetwork for fully-automated tooth segmentation. This, however, has the drawbackof ignoring the different geometric meanings provided by those raw attributes.This issue might possibly confuse the network in learning discriminativegeometric features and result in many isolated false predictions on the dentalmodel. Against this issue, we propose a two-stream graph convolutional network(TSGCNet) to learn multi-view geometric information from different geometricattributes. Our TSGCNet adopts two graph-learning streams, designed in aninput-aware fashion, to extract more discriminative high-level geometricrepresentations from coordinates and normal vectors, respectively. Thesefeature representations learned from the designed two different streams arefurther fused to integrate the multi-view complementary information for thecell-wise dense prediction task. We evaluate our proposed TSGCNet on areal-patient dataset of dental models acquired by 3D intraoral scanners, andexperimental results demonstrate that our method significantly outperformsstate-of-the-art methods for 3D shape segmentation.

TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation