A geometric calibration apparatus comprising: a phantom with a plurality of first markers; a memory configured to store one or more instructions; and a processor configured to, by executing the one or more instructions: detect a plurality of first points from a plurality of projection regions, respectively, wherein the plurality of first points correspond to respective first markers which have been projected onto the projection regions in two dimensions by a computer tomography device; calculate an output vector representing a probability distribution that gives a probability with which each of the first points is a projection of each of the first markers, by inputting data corresponding to each of the first points to a learning model that has been trained using training data that have, as an input data, data corresponding to points into which the first markers are respectively transformed by a randomly-generated projection matrix and have, as correct answer labels, values indicating classification of the first markers; extract a predetermined number of first samples from a fourth set including indices of the first points based on the probability distribution; obtain a candidate projection matrix on the basis of correspondences between second markers determined based on the first samples among the first markers and second points determined based on the first samples among the first points; calculate third points into which the first markers are transformed by the candidate projection matrix; calculate a difference between a first set of the third points and a second set of the first points; repeat a process of extracting the first samples, obtaining the candidate projection matrix, calculating the third points, and calculating the difference, in response to the difference being greater than a threshold; and designate the candidate projection matrix as a projection matrix in response to the difference being less than or equal to the threshold. a phantom with a plurality of first markers; a memory configured to store one or more instructions; and a processor configured to, by executing the one or more instructions: detect a plurality of first points from a plurality of projection regions, respectively, wherein the plurality of first points correspond to respective first markers which have been projected onto the projection regions in two dimensions by a computer tomography device; calculate an output vector representing a probability distribution that gives a probability with which each of the first points is a projection of each of the first markers, by inputting data corresponding to each of the first points to a learning model that has been trained using training data that have, as an input data, data corresponding to points into which the first markers are respectively transformed by a randomly-generated projection matrix and have, as correct answer labels, values indicating classification of the first markers; extract a predetermined number of first samples from a fourth set including indices of the first points based on the probability distribution; obtain a candidate projection matrix on the basis of correspondences between second markers determined based on the first samples among the first markers and second points determined based on the first samples among the first points; calculate third points into which the first markers are transformed by the candidate projection matrix; calculate a difference between a first set of the third points and a second set of the first points; repeat a process of extracting the first samples, obtaining the candidate projection matrix, calculating the third points, and calculating the difference, in response to the difference being greater than a threshold; and designate the candidate projection matrix as a projection matrix in response to the difference being less than or equal to the threshold. detect a plurality of first points from a plurality of projection regions, respectively, wherein the plurality of first points correspond to respective first markers which have been projected onto the projection regions in two dimensions by a computer tomography device; calculate an output vector representing a probability distribution that gives a probability with which each of the first points is a projection of each of the first markers, by inputting data corresponding to each of the first points to a learning model that has been trained using training data that have, as an input data, data corresponding to points into which the first markers are respectively transformed by a randomly-generated projection matrix and have, as correct answer labels, values indicating classification of the first markers; extract a predetermined number of first samples from a fourth set including indices of the first points based on the probability distribution; obtain a candidate projection matrix on the basis of correspondences between second markers determined based on the first samples among the first markers and second points determined based on the first samples among the first points; calculate third points into which the first markers are transformed by the candidate projection matrix; calculate a difference between a first set of the third points and a second set of the first points; repeat a process of extracting the first samples, obtaining the candidate projection matrix, calculating the third points, and calculating the difference, in response to the difference being greater than a threshold; and designate the candidate projection matrix as a projection matrix in response to the difference being less than or equal to the threshold.