  The ability to segment teeth precisely from digitized 3D dental models is an
essential task in computer-aided orthodontic surgical planning. To date, deep
learning 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-stream
network for fully-automated tooth segmentation. This, however, has the drawback
of ignoring the different geometric meanings provided by those raw attributes.
This issue might possibly confuse the network in learning discriminative
geometric features and result in many isolated false predictions on the dental
model. Against this issue, we propose a two-stream graph convolutional network
(TSGCNet) to learn multi-view geometric information from different geometric
attributes. Our TSGCNet adopts two graph-learning streams, designed in an
input-aware fashion, to extract more discriminative high-level geometric
representations from coordinates and normal vectors, respectively. These
feature representations learned from the designed two different streams are
further fused to integrate the multi-view complementary information for the
cell-wise dense prediction task. We evaluate our proposed TSGCNet on a
real-patient dataset of dental models acquired by 3D intraoral scanners, and
experimental results demonstrate that our method significantly outperforms
state-of-the-art methods for 3D shape segmentation.
