基于级联互信息关注网络的三维点云配准

Xiang Pan, Xiaoyi Ji, Sisi Cheng
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引用次数: 1

摘要

在三维点云配准中,如何提高两点云的局部特征相关性是一个具有挑战性的问题。本文提出了一种级联互信息关注配准网络。该网络通过叠加残差结构和使用横向连接来提高点云配准的精度。首先,对局部点集采用球面表示定义局部参考坐标系,提高了局部特征在噪声作用下的稳定性和可靠性;其次,利用注意力结构提高网络深度,保证网络的收敛性。此外,在网络中引入横向连接,避免了连接过程中特征的丢失。在实验部分,比较了不同算法的结果。实验结果表明,所提出的级联网络可以增强不同点云之间局部特征的相关性。与DCP和其他典型的配准算法相比,该算法的配准精度得到了显著提高。
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3D Point Cloud Registration Based on Cascaded Mutual Information Attention Network
For 3D point cloud registration, how to improve the local feature correlation of two point clouds is a challenging problem. In this paper, we propose a cascaded mutual information attention registration network. The network improves the accuracy of point cloud registration by stacking residual structure and using lateral connection. Firstly, the local reference coordinate system is defined by spherical representation for the local point set, which improves the stability and reliability of local features under noise. Secondly, the attention structure is used to improve the network depth and ensure the convergence of the network. Furthermore, a lateral connection is introduced into the network to avoid the loss of features in the process of concatenation. In the experimental part, the results of different algorithms are compared. It can be found that the proposed cascaded network can enhance the correlation of local features between different point clouds. As a result, it improves the registration accuracy significantly over the DCP and other typical algorithms.
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