Abstract: A method of generating a 3D model for digital dentistry using a virtual bridge based multi input Boolean operation, includes generating a first group model by generating a first virtual bridge connecting models spaced apart from each other among first input models of a first input group when the models are spaced apart from each other among the first input models, generating a second group model by generating a second virtual bridge connecting models spaced apart from each other among second input models of a second input group when the models are spaced apart from each other among the second input models, generating a first result model by a Boolean operation of the first group model and the second group model and removing a remaining first virtual bridge or a remaining second virtual bridge when the first virtual bridge or the second virtual bridge remains in the first result model.

Abstract: An automated method for aligning 3D (three-dimensional) dental data includes extracting landmark points of a CT (computerized tomography) data, extracting landmark points of scan data of a digital impression model, determining an up vector representing a direction of a patient's eyes and nose and identifying left and right of the landmark points of the scan data, extracting a teeth portion of the scan data, searching a source point of the scan data on a spline curve of the CT data to generate a candidate target point group and determining the candidate target point group having a smallest error with the landmark points of the CT data as a final candidate.

Abstract: An automated method for aligning 3D (three-dimensional) dental data includes extracting landmark points of a CT (computerized tomography) data, extracting landmark points of scan data of a digital impression model, determining an up vector representing a direction of a patient's eyes and nose and identifying left and right of the landmark points of the scan data, extracting a teeth portion of the scan data, searching a source point of the scan data on a spline curve of the CT data to generate a candidate target point group and determining the candidate target point group having a smallest error with the landmark points of the CT data as a final candidate.

Abstract: An automated method for generating a prosthesis from a 3D scan data, the method includes extracting prep information of a prepared tooth from the 3D scan data, generating a two dimensional (“2D”) projection images by projecting the 3D scan data based on the prep information and generating a 3D prosthesis based on the 2D projection images using a generative adversarial network including a 2D encoder and a 3D decoder.

Abstract: Provided are a method and a system for transmitting multiple data, in which the method includes receiving a plurality of transmission files for transmission from a transmission device of the first network to a reception device of the second network, and temporarily storing the received files, generating flexible packets by dividing each of the plurality of transmission files by a flexible packet length determined according to size of the files, in which a transmission file in a size smaller than the flexible packet length among the plurality of transmission files is generated as one flexible packet without being divided, loading the flexible packets into a plurality of flexible frames based on a corresponding transmission file priority according to a maximum data transmission size, and transmitting the plurality of flexible frames to the second network.

Abstract: An automated method for aligning a 3D dental library model to 3D oral scan data includes determining a valid tooth of the 3D oral scan data, extracting scan landmarks of the 3D oral scan data, loading a dental library model corresponding to the valid tooth of the 3D oral scan data, extracting library landmarks of the 3D dental library model, initial-aligning the 3D dental library model to the 3D oral scan data using the scan landmarks and the library landmarks and matching an individual tooth of the 3D dental library model and an individual tooth of the 3D oral scan data.

Abstract: Provided are a method and a system for transmitting multiple data, in which the method includes receiving a plurality of transmission files for transmission from a transmission device of the first network to a reception device of the second network, and temporarily storing the received files, generating flexible packets by dividing each of the plurality of transmission files by a flexible packet length determined according to size of the files, in which a transmission file in a size smaller than the flexible packet length among the plurality of transmission files is generated as one flexible packet without being divided, loading the flexible packets into a plurality of flexible frames based on a corresponding transmission file priority according to a maximum data transmission size, and transmitting the plurality of flexible frames to the second network.

Abstract: A method of generating a 3D model for digital dentistry using a virtual bridge based multi input Boolean operation, includes generating a first group model by generating a first virtual bridge connecting models spaced apart from each other among first input models of a first input group when the models are spaced apart from each other among the first input models, generating a second group model by generating a second virtual bridge connecting models spaced apart from each other among second input models of a second input group when the models are spaced apart from each other among the second input models, generating a first result model by a Boolean operation of the first group model and the second group model and removing a remaining first virtual bridge or a remaining second virtual bridge when the first virtual bridge or the second virtual bridge remains in the first result model.

Abstract: An automated method for aligning 3D (three-dimensional) dental data includes extracting landmark points of a CT (computerized tomography) data, extracting landmark points of scan data of a digital impression model, determining an up vector representing a direction of a patient's eyes and nose and identifying left and right of the landmark points of the scan data, extracting a teeth portion of the scan data, searching a source point of the scan data on a spline curve of the CT data to generate a candidate target point group and determining the candidate target point group having a smallest error with the landmark points of the CT data as a final candidate.