When abnormal conditions occur in point clouds

the normal adjustment may have erroneous results when using the minimum spanning tree algorithm

while the efficiency is low when using the surface reconstruction algorithm. In order to solve this problem

an optimal algorithm for normal adjustment of point cloud is proposed. It deals with three abnormal conditions separately. For the thin feature condition

it exacts feature points and reverses orientations compulsively. For the perpendicular normal condition

the neighboring region is expanded to get the tendency of the normal. For close-by surfaces condition

ambiguous neighbors are removed from the K-nearest neighbors to avoid creating an erroneous minimum spanning tree edge. Experiments show that the algorithm can adjust the normals correctty even when such abnormal conditions exist. Compared with surface reconstruction algorithm

the algorithm can adjust the normals more efficiently.