A computer-implemented method for automatically determining a canonical pose of a 3D dental structure represented by data points of a 3D data set, the method comprising: providing one or more blocks of data points of the 3D data set associated with a first coordinate system to the input of a first 3D deep neural network, the first 3D deep neural network being trained to generate canonical pose information associated with a canonical coordinate system defined relative to a position of part of a 3D dental structure and with a canonical representation of the 3D dental structure, wherein the size of the 3D dental structure is scaled and wherein one or more predetermined features of the canonical representation of the 3D dental structure are in alignment with axes of the canonical coordinate system; receiving canonical pose information from the output of the first 3D deep neural network, the canonical pose information comprising for each data point of the one or more blocks a prediction of a position of a data point in the canonical coordinate system, the position of the data point being defined by canonical coordinates; using the canonical coordinates to determine an orientation and scaling of the axes of the canonical coordinate system and a position of the origin of the canonical coordinate system relative to the axis and the origin of the first coordinate system and using the orientation and the position to determine transformation parameters, preferably rotation, translation and/or scaling parameters, for transforming coordinates of the first coordinate system into canonical coordinates; and, determining a canonical representation, preferably a canonical voxel representation or a canonical 3D mesh representation, of the 3D dental structure the determining including applying the transformation parameters to coordinates of the data points of the 3D data set. providing one or more blocks of data points of the 3D data set associated with a first coordinate system to the input of a first 3D deep neural network, the first 3D deep neural network being trained to generate canonical pose information associated with a canonical coordinate system defined relative to a position of part of a 3D dental structure and with a canonical representation of the 3D dental structure, wherein the size of the 3D dental structure is scaled and wherein one or more predetermined features of the canonical representation of the 3D dental structure are in alignment with axes of the canonical coordinate system; receiving canonical pose information from the output of the first 3D deep neural network, the canonical pose information comprising for each data point of the one or more blocks a prediction of a position of a data point in the canonical coordinate system, the position of the data point being defined by canonical coordinates; using the canonical coordinates to determine an orientation and scaling of the axes of the canonical coordinate system and a position of the origin of the canonical coordinate system relative to the axis and the origin of the first coordinate system and using the orientation and the position to determine transformation parameters, preferably rotation, translation and/or scaling parameters, for transforming coordinates of the first coordinate system into canonical coordinates; and, determining a canonical representation, preferably a canonical voxel representation or a canonical 3D mesh representation, of the 3D dental structure the determining including applying the transformation parameters to coordinates of the data points of the 3D data set.