In the target recognition of ladar, the accurate estimation of target pose can effectively simplify the recognition process. The existing PDVA algorithm as a method of target 3D pose estimation is mainly for ground structured targets. This method uses the planar normals of rigid targets as the vectors in the positive direction of the axes in model coordinate system(MCS) to estimate the 3D pose angles of targets, and its effectiveness has been verified by experiments. However, it is time consuming when determining the positive direction vectors of the axes in MCS and affecting the efficiency of the algorithm. In this paper, an improved PDVA algorithm was proposed and a method of clustering center neighborhood discriminant(CCND) was used for accelerating the determination process of positive direction vectors of the axes in MCS. The simulation experiments were performed with four military vehicle models. The results show that the average running time of the improved PDVA algorithm only accounts for about 66% of the PDVA algorithm, and it greatly improves the efficiency of target 3D pose estimation.