Point clouds generated from 3D scans of part surfaces consist of discrete points, some of which may be outliers. Filtering techniques to remove these outliers from point clouds frequently require a “guess and check” method to determine proper filter parameters. This paper presents two novel approaches to automatically determine proper filter parameters using the relationships between point cloud outlier removal, principal component variance, and the average nearest neighbor distance. Two post-processing workflows were developed that reduce outlier frequency in point clouds using these relationships. These post-processing workflows were applied to point clouds with artificially generated noise and outliers, as well as a real-world point cloud. Analysis of the results showed the approaches effectively reduced outlier frequency while preserving the ground truth surface, without requiring user input.