Detecting 3D keypoints is a long-standing challenge in computer vision. Most detectors end with a heuristic post-processing step that is not learned. We propose a 3D keypoint detector that improves on this step with a learned suppression module, paired with a Point Transformer backbone that we extend with a directional graph neural network. The module is a graph network over candidates that learns which to keep, which to suppress, and how to relocate the remaining ones. Paired with three backbones, it improves over DBSCAN and greedy non-maximum suppression, and because it operates on candidate features rather than raw geometry, the same formulation applies to both structural and semantic keypoints. Our model surpasses the per-category trained KeypointDETR on 12 of 16 KeypointNet categories, attains the best Corner F1 on the Building3D Entry-Level benchmark, and remains competitive with BWFormer on the larger Tallinn split. GitHub implementation: https://github.com/cansdev/learned-suppression-3d.

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