DAMR: A deep gaussian mixture point cloud registration method with the dual attention mechanism

Abstract

Point cloud registration is important for the processing of 3D model reconstruction, but with the challenge of low registration accuracy. In order to overcome this obstacle, this paper proposed a dual attention mechanism registration (DAMR) method for the point cloud registration. Firstly, the dual attention mechanism is utilized to extract key point features with different dimensions via assigning weights to the input point clouds. Secondly, the matching parameters are obtained by estimating the position correspondence between feature points and gaussian mixture model. Finally, the parameter unit blocks of two models are applied to recover the optimal transformation by matching parameters. In order to test the performance of our method, four groups of datasets include public data models and cultural relic models are adopted, respectively. Compared with traditional methods, our method has shown better performance to effectively ensured the registration accuracy of 3D point cloud models.