In this article, we propose a method for comparing arbitrary sets of point clouds based on pattern descriptors. The method is specifically designed to process large outdoor 3D laser scans characterized by highly variable point density, which changes with distance from the scan center. To generate pattern descriptors, we utilize a modified encoder block from a Vision in Transformer model, trained with a differential loss function. The model is trained on scanner-specific data, enabling it to generalize to any scans without requiring retraining.