A reconstructed 3D point cloud model based on image usually includes many outliers. These outliers may exist in isolation or dense aggregation forming point clusters, and they may also be distributed around the model or even on the mode surface.It is difficult to filter out outliers of various distribution states through one detection method. Therefore, a comprehensive outlier detection algorithm is proposed. Firstly, the outliers far from the main body of the model are eliminated by space distance, and the search speed of outliers is accelerated by constructing the spatial topological relationship.Then, the boundary matching method is used to filter out the outliers around the model by comparing the smaller clusters with the largest ones respectively. Finally, an improved K-means algorithm is used to cluster and classify the point cloud data according to RGB color value features, and the outliers adhering to the model surface are detected and filtered by combining the identified outliers.Simulation results show that the proposed algorithm can effectively filter out the outliers with multiple distribution states in the reconstructed point cloud.