  Precise segmentation of teeth from intra-oral scanner images is an essential
task in computer-aided orthodontic surgical planning. The state-of-the-art deep
learning-based methods often simply concatenate the raw geometric attributes
(i.e., coordinates and normal vectors) of mesh cells to train a single-stream
network for automatic intra-oral scanner image segmentation. However, since
different raw attributes reveal completely different geometric information, the
naive concatenation of different raw attributes at the (low-level) input stage
may bring unnecessary confusion in describing and differentiating between mesh
cells, thus hampering the learning of high-level geometric representations for
the segmentation task. To address this issue, we design a two-stream graph
convolutional network (i.e., TSGCN), which can effectively handle inter-view
confusion between different raw attributes to more effectively fuse their
complementary information and learn discriminative multi-view geometric
representations. Specifically, our TSGCN adopts two input-specific
graph-learning streams to extract complementary high-level geometric
representations from coordinates and normal vectors, respectively. Then, these
single-view representations are further fused by a self-attention module to
adaptively balance the contributions of different views in learning more
discriminative multi-view representations for accurate and fully automatic
tooth segmentation. We have evaluated our TSGCN on a real-patient dataset of
dental (mesh) models acquired by 3D intraoral scanners. Experimental results
show that our TSGCN significantly outperforms state-of-the-art methods in 3D
tooth (surface) segmentation. Github:
https://github.com/ZhangLingMing1/TSGCNet.
