The application discloses a face
soft tissue defect
point cloud completion method based on a PF-Net, and belongs to the technical field of face
soft tissue defect completion. The method comprises the following steps: S1, a
data preparation stage: a data enhancement
algorithm is designed to obtain a
paired data set of incomplete input and defect labels; S2, a model construction stage: a dynamic graph
convolution and a boundary attention mechanism are introduced into an
encoder to optimize a
point cloud completion
network model PF-Net; S3, a model training stage: a
training set is input into the PF-Net model for training; S4, a
model testing stage: a
test set is input into a generator for testing; and S5, a
model inference stage: for clinical face soft defect
point cloud data, multi-scale input data are acquired and completion is performed. The application realizes autonomous
simulation design of face
soft tissue defects, solves technical barriers in existing digital prosthetic technology, such as dependence on healthy side
mirror image data or standard organ models, and solves the problem of unstable repair effect caused by dependence on doctor experience and aesthetic literacy.