Abstract: The registration of 3D point cloud data is an important research topic in the field of computer vision and a key step in 3D reconstruction. Aiming at the registration problem of RGBD point cloud data, a coarse registration method based on feature similarity is proposed. Firstly, the curvature and color characteristics of the RGBD point cloud model to be registered should be calculated. Through the statistical analysis of color characteristics, if the color features of the model are rich enough, the color similarity strategy will be adopted first, otherwise, the curvature similarity strategy will be tried. The feature point extraction can simplify the point cloud model. And we will use the corresponding point selection strategy to select all corresponding point pairs. The coarse registration matrix is obtained by adopting the optimized sample consensus algorithm on the candidate corresponding pairs, and the coarse registration of the two point clouds is realized. For the RGBD point cloud model with different colors and texture, this method can adaptively select the appropriate feature point selection strategy to realize the good coarse registration between point clouds. For different models, we can adaptively select the corresponding selection strategy to calculate the transformation matrix and complete the coarse registration. The experimental results show that the proposed method can adaptively select the color similarity strategy to complete the coarse registration for the RGBD model with less geometric features. For different types of model, the registration results are better, and the algorithm is more efficient.

Key words: RGBD point cloud, coarse registration, feature similarity, color similarity, curvature similarity