Viewpoint planning plays an important role in automatic 3-D model generation. In a previous paper[18], we have proposed a viewpoint planning method to determine the next-best-viewpoint (NBV) for incremental model construction. Based on a current partial model, this algorithm provides quantitative evaluations on the suitability of viewpoints as the NBV. Since the evaluation is performed for all potential viewpoints, the NBV planning is very time-consuming. In this paper, we present a new method of discretizing a spherical view space by a look-up array which will highly facilitate the NBV evaluations. Two main issues are addressed: 1) a uniform tessellation of the spherical space and its mapping onto the 2-D array; 2) incremental updating computations for evaluating viewpoints as the NBV. The efficiency of the method is verified by algorithmic analyses and experiments using a real modeling system.