Abstract
3D point cloud registration is a crucial task in computer vision, robotics, and safeguarding cultural artifacts in the digital realm. However, maintaining a balance between efficiency and accuracy during the 3D point cloud registration process continues to be a challenge. To solve this issue, a Reliable Correspondences Evaluation and Feature Interaction Network called RCFI-Net has been proposed for fast and precise registration. Our model use a two-stage approach to improve registration efficiency. In the first stage, a Transformer with position encoding network is employed to reinforce point features. Overlapping masks are learned based on the attention mechanism to identify overlapping areas, followed by the sampling of interest points that have high scores in these areas to accelerate registration. In the second stage, non-distinguishing points are eliminated, and a triangulated descriptor is introduced to further differentiate inliers and outliers and find reliable correspondences. Our proposed method has been evaluated on both public datasets and real Terracotta Warriors data, with results showing that it outperforms traditional and feature-learning methods in terms of accuracy, efficiency, and robustness.
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