To address the problems of large registration errors and poor adaptability of the traditional iterative closest point （ICP） algorithm when point clouds overlap or partially overlap， an improved registration algorithm based on weighted optimization of matching point pairs is proposed. First， an improved voxel downsampling algorithm is proposed to sample point clouds， which reduces the amount of data and improves the robustness of the algorithm against noise. Then， the improved Sigmoid function is used to assign different weights to the matching point pairs participating in the registration， which overcomes the disadvantage of traditional algorithms that ignore matching point pairs with small distances still have wrong point pairs， while improves the registration accuracy and convergence speed. Finally， a method to solve registration parameters using singular value decomposition （SVD） is proposed to further improve registration accuracy. The registration and noise experiments with different overlapping degrees were performed， and the proposed algorithm was verified by combining the three-dimensional point cloud reconstruction of the crankshaft. The experimental results showed that， compared with the Tr-ICP and AA-ICP algorithms， the error in the proposed algorithm was reduced by approximately 34.1% and 29%， respectively. Further， the registration time was shortened by approximately 16.1% compared with the Tr-ICP algorithm. Hence， compared with traditional algorithms， the proposed algorithm has higher registration accuracy， better applicability， and robustness.