PREDATOR is a 3D point cloud registration method that uses attention mechanisms to identify overlapping regions between scans and predict correspondences. The architecture employs a transformer-based encoder to process point features, with cross-attention layers enabling each point to consider its context for improved matching. The model was trained on 3DMatch and other datasets for evaluating registration performance under challenging low-overlap conditions.