A Novel Point Cloud Registration Method Based on ROPNet

Point cloud registration is a crucial preprocessing step for point cloud data analysis and applications. Nowadays, many deep-learning-based methods have been proposed to improve the registration quality. These methods always use the sum of two cross-entropy as a loss function to train the model, which may lead to mismatching in overlapping regions. In this paper, we designed a new loss function based on the cross-entropy and applied it to the ROPNet point cloud registration model. Meanwhile, we improved the ROPNet by adding the channel attention mechanism to make the network focus on both global and local important information, thus improving the registration performance and reducing the point cloud registration error. We tested our method on ModelNet40 dataset, and the experimental results demonstrate the effectiveness of our proposed method.

Automated summary

This paper introduces a new loss function based on cross-entropy and applies it to the ROPNet point cloud registration model. Additionally, the ROPNet is improved by incorporating channel attention to focus on global and local important information, leading to enhanced registration performance and reduced registration error. Experimental results on the ModelNet40 dataset validate the effectiveness of the proposed method.