Prioritising geometric saliency: rethinking point sampling as a discriminative mechanism for 3D point cloud learning
Point cloud representation learning heavily relies on input sampling, yet mainstream strategies like random sampling and farthest point sampling (FPS) prioritize spatial coverage over geometric saliency, often discarding discriminative edges and fine structures. To address this, we propose Geometry-Aware Sampling (GAS)...