A self-adaption fast point cloud simplification algorithm based on normal eigenvalues

In order to simplify the dense data of point cloud efficiently, this paper proposes an algorithm which is quick and easy to simplify the point cloud based on the standard deviation of the normal vector. First of all, the normal distribution have to be calculated after getting the dense point cloud data which has been down sampled. Second of all, calculate the separation threshold value between the feature points and the other through the normal angle between the adjacent points. Finally, perform the down sampling step-by-step between feature points and the other to realize the self-adapting simplification of point cloud. According to the result, this algorithm has realized an efficient simplification of point cloud model in short time. And it has greatly held the characteristics and shape of the original model.