To address the issues of mismatched point pairs and cumulative errors encountered during the multi - view point registration of large - scale symmetrical objects , a multi-view point cloud registration algorithm based on depth sensor is proposed. Firstly , the proposed approach leverages depth sensors to capture multiple point clouds of the target object from various viewpoints , which are then subjected to a series of preprocessing steps. To achieve coarse registration , the Super 4-points congruent sets ( Super4PCS) is employed specifically for adjacent point clouds on one side of the object. Subsequently , an enhanced point-to-plane ICP algorithm is utilized to refine the registration by e- liminating erroneous point pairs. The resulting refined point clouds from the left and right sides are seamlessly com- bined , thereby generating a comprehensive 3D point cloud model. Furthermore , to mitigate the issue of cumulative er- rors arising from the multi-view registration process , a global optimization technique is introduced. Experimental eval- uations demonstrate the effectiveness and accuracy of the proposed method in achieving precise multi-view point cloud registration and generating highly accurate 3D point cloud models.