Abstract: In one aspect of the present application, a method for segmenting 3D digital model of jaw is provided, the method comprises: obtaining a 3D digital model of jaw to be segmented; converting the 3D digital model of jaw to be segmented into a point cloud; sampling the point cloud to obtain sample points; extracting features from the sample points; classifying the sample points based on the extracted features using a trained DGCNN network; and classifying other points in the point cloud based on the classified sample points using a KNN algorithm, where classifying a point is classifying a facet of the 3D digital model of jaw to be segmented represented by the point as a certain tooth or gingiva.

Abstract: In one aspect, the present application provides a computer-implemented method for generating a digital data set representing a target tooth arrangement, comprising: obtaining a first 3D digital model representing an initial tooth arrangement; extracting at least one feature from each tooth of the first 3D digital model; generating a feature vector based on the extracted features; and aligning the first 3D digital model using a Statistical Shape Model based on the feature vector to obtain a second 3D digital model representing a target tooth arrangement.

Abstract: The present application provides a computer-implemented method for generating a digital data set representing a target tooth arrangement, comprising: obtaining a first and a second 3D digital models respectively representing upper jaw teeth and lower jaw teeth under an initial tooth arrangement, where the first and the second 3D digital models are in a predetermined relative positional relationship; extracting a tooth level feature vector from each tooth of the first and second 3D digital models; preliminarily aligning the first and second 3D digital models based on the tooth level feature vectors using a trained first deep neural network; extracting a jaw level feature vector for each tooth of the preliminarily aligned first and second 3D digital models; and further aligning the preliminarily aligned first and second 3D digital models to obtain a target tooth arrangement based on the jaw level feature vectors using a trained second deep neural network.

Abstract: In one aspect, the present application provides a method for verifying a segmentation result of a 3D digital model of jaw, the method comprises: obtaining a reference position segmentation result of a 3D digital model of jaw, where the reference position result is obtained by segmenting the 3D digital model of jaw positioned at a reference position using a first segmentation method; perturbating the 3D digital model of jaw positioned at the reference position multiple times, and segmenting the 3D digital model of jaw positioned at the multiple perturbed positions using the first segmentation method, to obtain corresponding multiple perturbed position segmentation results; and determining whether the reference position segmentation result is reliable based on similarities between the reference position segmentation result and the multiple perturbed position segmentation results, where segmentation of the 3D digital model of jaw is to segment teeth from each other and from gingiva, and is to classify facets of

Abstract: In one aspect of the present application, there is provided a computer-implemented method for generating a digital data set representing a target tooth arrangement, the method comprises: obtaining a first and a second 3D digital models respectively representing upper jaw teeth and lower jaw teeth under an initial tooth arrangement, where the first and the second 3D digital models are in a predetermined relative position relationship; extracting features from the first and the second 3D digital models; generating a first digital data set representing a target tooth arrangement of the upper jaw teeth and the lower jaw teeth based on the extracted features using a trained recurrent neural network based deep learning neural network.