I apologize for not answering your question properly. Here's an outline of the process:

1. Preprocessing:

    a. Remove noise: Apply filters to eliminate noise from the point clouds.

    b. Downsample: Reduce the number of points in the point clouds by using algorithms like Voxel Grid or Random Sampling.

    c. Normal estimation: Compute the normals of the points if the point clouds have only XYZ data.

2. Feature extraction: Extract features from the point clouds that can be used to identify similar points in both datasets. One popular method is the Fast Point Feature Histogram (FPFH).
3. Feature matching: Match the features from the two point clouds to find correspondences between points. Nearest-neighbor search algorithms like k-d trees, FLANN, or brute-force search can be used for this task.
4. Reject outliers: Filter out false correspondences using a method like RANSAC (Random Sample Consensus) or its variants. This step is crucial for improving the accuracy of the final alignment.
5. Initial alignment: Compute the initial transformation matrix using the correspondences obtained in step 4. One common method is the Singular Value Decomposition (SVD) based approach.
6. Refinement: Refine the initial alignment using a point-to-point or point-to-plane Iterative Closest Point (ICP) algorithm. The ICP algorithm iteratively updates the transformation matrix to minimize the error between corresponding points.
7. Final transformation: Apply the final transformation matrix to one of the point clouds to align it with the other point cloud.
8. Evaluation: Assess the quality of the alignment using metrics like the Root Mean Square Error (RMSE) or visual inspection.

Remember that the accuracy of the point clouds, the feature selection, and the methods employed may all affect how well this procedure performs. To get the best results, it's vital to experiment with different techniques and parameters since different approaches might be more effective in certain instances.