一种使用伪对应点生成的3D点云配准方法

doi:10.11896/jsjkx.220700023

Abstract

Abstract: To address the challenging problems of point cloud registration in 3D reconstruction(e.g.,difficulty in finding corresponding points,etc.),this paper proposes a point cloud registration method based on cross-attention and artificial correspondence generation mechanism,Deep Artificial Correspondence Generation(DeepACG),by fully utilizing the geometric information of the source and target point clouds.Our method adopts a three-stage network model.The first stage is the deep feature encoding module,which exchanges and enhances the contextual and structural information between two unaligned point clouds using the cross-attention mechanism.The second stage is the artificial correspondence generation module,which synthesizes the artificial correspondences by weighting the soft mapping.The third one is the correspondence weighting and outlier filtering module,which assigns different weights to the correspondence pairs and rejects them with a small probability.Extensive experiments are conducted on both synthetic and real-world datasets.Our method achieves a registration recall of 92.61% on the real-world indoor dataset 3DMatch,and we execute unseen partial registration experiments on ModelNet40,reducing the root mean square error of the rotation matrix and translation vector to 0.016 and 0.000 09,respectively.Experimental results show that DeepACG has higher registration accuracy and robustness,and its alignment error is lower than that of the existing mainstream registration approaches.

Key words: Cross-attention, Artificial correspondence generation, Outlier filtering, 3D point cloud registration

CLC Number:

TP391

BAI Zhengyao, XU Zhu, ZHANG Yihan. Deep Artificial Correspondence Generation for 3D Point Cloud Registration[J].Computer Science, 2023, 50(9): 210-219.

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