Abstract: Aiming at the problem that the point cloud data scanned by Kinect or other depth camera have a huge number and more noise, a feature preserving method for point cloud denoising and simplification was proposed. This algorithm classified the point cloud rapidly by K-D tree; find The corresponding surface curvature values were obtained using curvature estimation algorithm; The K-means clustering algorithm for point cloud clustering was used. For each point in the cluster, the Euclidean distance was depended on between the point and center of the cluster and the change of the near points curvature to determine whether the noise points. The point cloud data was simplified by the feature preserving method. The experimental results show that the denoising and feature preserving point cloud simplification method is quickly and efficiently, for the removal of a large number of external noise has a positive effect, and the streamline point cloud data have the retention of original point cloud features.

Key words: K-means clustering, point cloud denoising, point cloud simplification, RGBD data