Coarse registration of point cloud base on deep local extremum detection and attentive description

Coarse registration of point cloud is a necessary step for object digitization. However, insufficient overlapping, large pose difference and the existence of noise and outliers seriously reduce the result. In this paper, several improvements were made to improve the registration effect under the above conditions. Firstly, a lightweight network for feature point detection based on local extremum is proposed to improve the repeatability and robustness of feature detection; Secondly, a feature description network combined with attention mechanism is constructed to generate highly differentiated descriptors for the feature points; Finally, a transformation parameters calculation strategy based on only two feature points is proposed, which improves the success probability under low overlapping. Experiments show that our feature detection, description and registration methods achieved satisfactory results in various challenging scenes and perform better than current mainstream methods.