The three-dimensional pose information of target is more and more widely used in the fields of target motion analysis, target recognition and target tracking. The K-means algorithm based on distance was used to classify the point normal vector by the existing OPDVA algorithm, then the positive direction of the target coordinate system MCS was determined, and the three-dimensional pose angle of the target was obtained. In view of the unsatisfactory effect of point normal vector classification, a three-dimensional pose estimation algorithm based on mean shift point normal vector classification (PEMSPNC) was proposed. The Mean Shift algorithm was used, which did not depend on the initial parameter setting and based on density clustering, to classify the normal vectors of different plane points with different density distribution, and to find the normal vector of the point with the maximum density as the representative normal vector of each class to determine the positive direction of MCS, then the pose angle of the target was calculated, and the target size according to target pose estimation results was computered. The rectangle fitting method, OPDVA and PEMSPNC algorithm were used to test the simulated and measured range profiles. The results show that the pose estimation error obtained by using PEMSPNC algorithm is the smallest, and compared with OPDVA algorithm, the average error is reduced by 0.4434 °, and has a good processing result for the measured data.