Since it is difficult to denoise and simplify a three dimensional point cloud data by a same parameter, a new unified algorithm based on the Extended Surface Variation based Local Outlier Factor (ESVLOF) for denoising and simplification of scattered point cloud was proposed. Through the analysis of the definition of ESVLOF, its properties were given. With the help of the surface variability computed in denoising process and the default similarity coefficient, the parameter γ which decreased with the increase of surface variation was constructed. Then the parameter γ was used as local threshold for denoising and simplifying point cloud. The simulation results show that this method can preserve the geometric characteristics of the original data. Compared with traditional 3D point-cloud preprocessing, the efficiency of this method is nearly doubled.

Aiming at the problem of large amount of data and characteristics of surface in laser scattered point cloud, a K-Nearest Neighbors (KNN) searching algorithm for laser scattered point cloud was put forward to reduce memory usage and improve processing efficiency. Firstly, only the non-empty subspace numbers were stored by multistage classification and dynamic linked list storage. Adjacent subspace was coded in ternary, and the pointer connection between adjacent subspaces was established by dual relationship of code, a generalized table that contained all kinds of required information for KNN searching was constructed, then KNN were searched. In the process of KNN searching, the candidate points outside inscribed sphere of filtration cube were directly deleted when calculating the distance from measured point to candidate points, thus reducing the candidate points that participate in the sort by distance to half. Both dividing principles, whether it relies on K value or not, can be used to calculate different K neighborhoods. Experimental results prove that the proposed algorithm not only has low memory usage, but also has high efficiency.

Concerning that the original Surface Variation based Local Outlier Factor (SVLOF) cannot filter out the outliers on edges or corners of three-dimensional solid, a new near outlier detection algorithm of scattered point cloud was proposed. This algorithm firstly defined SVLOF on the k neighborhood-like region, and expanded the definition of SVLOF. The expanded SVLOF can not only filter outliers on smooth surface but also filter outliers on edges or corners of three-dimensional solid. At the same time, it still retains the space of threshold value enough of original SVLOF. The experimental results of the simulation data and measured data show that the new algorithm can detect the near outliers of scattered point cloud effectively without changing the efficiency obviously.