Point Cloud Registration Network Combining Channel Priority Convolution Attention
The limitations of point cloud registration networks in processing large-scale point clouds and capturing local detail features,resulted in insufficient registration accuracy for overlapping areas of point clouds,so a new point cloud registration network CR-RORNet was proposed.This network combined channel first convolutional attention and ResPointNet module,and overcame the shortcomings of existing methods in dealing with complex scenes and irregular point clouds,and improved the registration performance for point clouds with significant initial pose differences.Firstly,in the coarse registration stage,the ResPointNet module was designed to enhance the extraction and fusion of global and multi-level features of the point cloud model by introducing residual connection mechanism.Secondly,in the dynamic graph convolutional neural network,channel prior convolutional attention CPCA and cross stage gradient aggregation mechanism were combined.The CPCA mechanism utilized channel prior information to strengthen the network's attention to important feature channels and regions.When dealing with overlapping parts of point clouds,it could effectively enhance the network model's ability to capture local detail features of point clouds and suppress the influence of low confidence regions,significantly improving the registration effect;the cross stage gradient aggregation mechanism integrated gradient information from different depth levels of point cloud models,and ensured that the network could fully understand the structure and local details of the point cloud when dealing with small components or largescale scene point cloud models.And the learned features had good expressive power,so high-precision registration of point cloud data in complex scenes was realized.Experimental results show that CR-RORNet performs better than other point cloud registration methods on self collected datasets.Compared to baseline RORNet,CR-RORNet reduces RMSE(t)error by 39.5%and MSE(R)error by 5.1%.Experiment results on the publicly available dataset ModelNet40 show that the network has good generalization performance.
point cloud registrationdeep learningattention mechanismresidual network
万方数据
维普
