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IntroductionPoint cloud registration is a crucial step in many computer visio...

IntroductionPoint cloud registration is a crucial step in many computer vision and robotics applications, such as simultaneous localization and mapping (SLAM), 3D reconstruction, and object recognition. The goal of point cloud registration is to align multiple partially overlapping point clouds to create a larger and more complete representation of the environment. While various algorithms have been proposed for point cloud registration, achieving accurate and efficient registration remains challenging, especially in the presence of noise, outliers, and partial overlap.Problem StatementOne common approach to point cloud registration is to find correspondences between points in different clouds and then estimate a rigid transformation that aligns these correspondences. However, estimating correspondences accurately can be difficult due to noise and outliers. This can lead to incorrect alignments and poor registration results. Therefore, it is crucial to identify and leverage reliable correspondences while discarding outliers to improve registration accuracy.Proposed MethodIn this study, we propose a novel method for point cloud registration that leverages the inlier correspondences proportion to improve the accuracy of the alignment. Our method consists of the following steps:Correspondence EstimationInitially, we estimate correspondences between points in the reference and target point clouds using a robust feature matching algorithm. This algorithm takes into account geometric and photometric properties of the points to find reliable correspondencesOutlier RejectionAfter estimating correspondences, we apply an outlier rejection technique to identify and discard outliers. We compute a similarity matrix that measures the similarity of each point pair and then use a predefined threshold to classify points as inliers or outliersInlier Correspondences Proportion CalculationOnce the outliers are removed, we calculate the proportion of inlier correspondences. This is done by dividing the number of inlier correspondences by the total number of correspondencesTransformation EstimationBased on the inlier correspondences, we estimate a rigid transformation that aligns the reference point cloud with the target. We use an iterative closest point (ICP) algorithm to find the optimal transformation that minimizes the overall registration errorIterative RefinementTo further improve the alignment accuracy, we iteratively refine the estimate obtained in the previous step. We repeat steps 1-4 multiple times, gradually improving the registration accuracy until convergence is reachedExperimental EvaluationWe conducted comprehensive experiments to evaluate the performance of our proposed method on a variety of synthetic and real-world datasets. The results demonstrate that leveraging the inlier correspondences proportion significantly improves the registration accuracy compared to existing state-of-the-art methods. Our method achieves higher precision and robustness, even in cases with high noise levels and outliers.ConclusionIn this paper, we presented a novel method for point cloud registration that leverages the inlier correspondences proportion to improve alignment accuracy. Our method effectively removes outliers, accurately estimates the proportion of inliers, and achieves highly accurate and robust registrations. The experimental results demonstrate the superiority of our approach compared to existing methods. Our method has the potential to improve various computer vision and robotics applications that rely on point cloud registration.