Outlier detection for reconstructed point clouds based on image
Xinggang Li, Yaping Zhang, Yuwei Yang · 2017
A 3D point cloud model reconstructed based on image usually contains many outliers. These outliers may exist in the form of isolation or aggregated clusters, or connected to surface of model. Based on the distribution characteristics of outliers, an integrated outlier detection method is proposed. Firstly, the spatial topological relation of point cloud data is established by dividing cell method. When the surface of model sampling is insufficient, the points of surface will be split into a number of clusters with different sizes instead of a complete cluster, the boundary matching method can be used to preserve the good points and remove outlier clusters. Finally, the improved K-means algorithm is adopted to cluster all points based on colors, and remove the outliers which are connected to surface. Simulation results show that the proposed algorithm can effectively detect outliers with the different distribution state in the reconstructed point clouds.