Aiming at the problems of high time complexity, slow convergence speed and error-prone matching of low coverage point cloud registration, a point cloud registration algorithm based on region segmentation was proposed. Firstly, the volume integral invariant was used to calculate the concavity and convexity of points on the point cloud, and then the concavity and convexity feature point sets were extracted. Secondly, the regions of the feature points were partitioned by the segmentation algorithm based on the mixed manifold spectral clustering, and the regions were registered by the Iterative Closest Point (ICP) algorithm based on Singular Value Decomposition (SVD), so that the accurate registration of point clouds could be achieved. The experimental results show that the proposed algorithm can greatly improve the coverage of point clouds by region segmentation, and the optimal rotation matrix of rigid body transformation can be calculated without iteration. The algorithm has the registration accuracy increased by more than 10% and the registration time reduced by more than 20%. Therefore, the proposed algorithm can achieve fast and accurate registration of point clouds with low coverage.

To improve the retrieval precision of 3D model with the complex surface, a new method based on curvedness feature was proposed. First, the sample points were obtained on the 3D model surface. The curvedness of these points was obtained by computing Gauss curvature and Mean curvature. The curvedness values showed properties of 3D model surface. Secondly, the centroid of the model was set as the center. The coordinate system in which two coordinate axes were the curvedness value and the Euclid distance between the random point and the center was constructed. The distribution matrix of curvedness feature was obtained by computing the statistical number of the sample points in the different Euclid distance. This distribution matrix was the feature descriptor of the 3D model. This descriptor had the property of rotation invariance and translation invariance, which could well reflect the geometric characteristics of complex surfaces. Finally, the similarity between different models was given by comparing the curvedness distribution matrix. The experimental results show that the proposed method can effectively improve the accuracy of the 3D model retrieval, especially suitable for those models with complex surfaces.