  Automatic tooth instance segmentation on 3D dental models is a fundamental
task for computer-aided orthodontic treatments. Existing learning-based methods
rely heavily on expensive point-wise annotations. To alleviate this problem, we
are the first to explore a low-cost annotation way for 3D tooth instance
segmentation, i.e., labeling all tooth centroids and only a few teeth for each
dental model. Regarding the challenge when only weak annotation is provided, we
present a dental arch prior-assisted 3D tooth segmentation method, namely
DArch. Our DArch consists of two stages, including tooth centroid detection and
tooth instance segmentation. Accurately detecting the tooth centroids can help
locate the individual tooth, thus benefiting the segmentation. Thus, our DArch
proposes to leverage the dental arch prior to assist the detection.
Specifically, we firstly propose a coarse-to-fine method to estimate the dental
arch, in which the dental arch is initially generated by Bezier curve
regression, and then a graph-based convolutional network (GCN) is trained to
refine it. With the estimated dental arch, we then propose a novel Arch-aware
Point Sampling (APS) method to assist the tooth centroid proposal generation.
Meantime, a segmentor is independently trained using a patch-based training
strategy, aiming to segment a tooth instance from a 3D patch centered at the
tooth centroid. Experimental results on $4,773$ dental models have shown our
DArch can accurately segment each tooth of a dental model, and its performance
is superior to the state-of-the-art methods.
