A three-dimensional tooth model segmentation method of a double-
branch geometric
attention network based on
centroid guidance is oriented to
oral cavity three-dimensional scanning
point cloud data and comprises the steps that firstly, normalization and normal vector
estimation are conducted on the
oral cavity three-dimensional
point cloud data, a double-
branch encoder for coordinate and normal decoupling is constructed, and a global topological structure and local geometric features are extracted respectively; secondly, a separable attention mechanism guided by the
mass center is introduced into a coordinate
branch so as to improve the distinguishing ability of adjacent teeth, and a graph
convolution attention mechanism is introduced into a normal branch so as to strengthen the boundary expression of the teeth and gingiva; further, in the fusion stage, a
mass center thermodynamic diagram and multi-scale
feature aggregation are combined, and joint modeling of global and local structures is achieved; and finally, collaborative optimization of instance segmentation and
centroid localization tasks is carried out through a dynamically weighted joint
loss function. According to the method, the segmentation precision and robustness under complex cases are remarkably improved while the light weight is kept, and the
automation and clinical practicability of
oral diagnosis and treatment are enhanced.