Point cloud registration plays a crucial role in the field of computer vision, covering multiple areas such as 3D reconstruction, target recognition, and robot navigation. With the advancement of sensor technology, the acquisition of dense point cloud data has become easier, and the geometric details of models are more precise. However, the processing of dense point clouds faces many challenges, such as high computational complexity and low efficiency. To address this, this paper proposes a dense point cloud registration algorithm based on distance tramsform. The algorithm first projects the source point cloud and the target point cloud through distance tramsform technology to obtain distance tramsform maps from different perspectives. Then, taking the difference between the two maps as the objective function, the gradient descent algorithm is used to optimize the tramsform relationship between point clouds. After multiple iterations, accurate alignment is achieved. Experiments on the Stanford 3D Scanning dataset show that this algorithm has high precision and fast speed when processing dense point clouds. Registering hundreds of thousands of point clouds only takes about 2 seconds, and the error is less than 0.3° and 0.5 mm. It has broad application prospects.