Deep Feature Interaction Network for Point Cloud Registration, With Applications to Optical Measurement of Blade Profiles

Optical measurement methods for blade profiles attract lots of interest in industry. Due to the nature of the thin-walled and twisted spatial freeform surfaces of blades, the measurement accuracy would be significantly affected by the accumulated error associated with the geometric accuracy and motion stability of the developed multiview system. To overcome these issues, this article proposes a deep feature interaction network for fine registration of the multiview data. In our network, we design a two-branch structure to integrate a global and a local feature extraction branch to encode point cloud features. Moreover, we propose a feature interaction module to strengthen information association between two point clouds during feature extraction. Next, an attention mechanism is used to fuse matching information between two matching matrices obtained from the global-based and the local-based features. Experimental results demonstrate the feasibility and good practical application prospect of this method.

Automated summary

This article proposes a deep feature interaction network for fine registration of multiview data, specifically designed for the thin-walled and twisted spatial freeform surfaces of blades. The network uses a two-branch structure to integrate global and local feature extraction branches and a feature interaction module to strengthen information association between two point clouds. Experimental results demonstrate the feasibility and good practical application prospect of this method.