A method for automatically determining a canonical pose of a 3D object represented by a 3D data set is described, wherein the method comprises: providing one or more blocks of voxels of a voxel representation of the3D object associated with a first coordinate system to the input of a first 3D deep neural network, the first 3D neural network being trained to generate canonical pose information associated with a canonical coordinate system defined relative to a position of part of the 3D dental structure; receiving canonical pose information from the output of thefirst3D deep neural network, the canonical pose information comprising for each voxel of the one or more blocks a prediction of a position of the voxel in the canonical coordinate system, the position being defined by canonical coordinates; using the canonical coordinates to determine an orientation and scale 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 3D coordinate system and using the orientation and the position to determine transformation parameters for transforming coordinates of the first coordinate system into canonical coordinates; and, determining a canonical representation of the 3D dental structure, the determining including applying the transformation parameters to coordinates of the voxels of the voxel representation or the 3D data set used for determining the voxel representation.
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