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3D data representation becomes more popular, but the enormous points make the model reconstruction and the object recognition difficult. A simplification algorithm for 3D point cloud data integrating both the feature parameter and uniform spherical sampling is presented. At first, we define a feature parameter which includes the average distance parameter and the normal included angle parameter between the point and its neighboring points and point curvature parameter. Then we calculate the density of 3D point cloud data and define it as the feature threshold. We distinguish the feature from the non-feature points by comparing the feature parameter and the threshold. Then we simplify the non-feature points by uniform spherical sampling. The non-feature sampling result and the feature points will be used as the final simplification result. The experimental results demonstrate that our new approach might retain the sharp information of the 3D point cloud data.