Edge-embedded point cloud sampling with DINOv3 constraints

Point cloud sampling plays a crucial role in reducing computational cost while maintaining geometric and semantic fidelity for downstream 3D tasks. Traditional task-agnostic methods, such as Farthest Point Sampling (FPS), often suffer from the loss of discriminative structures, while task-specific approaches require additional training and lack generality. Even the emerging task-agnostic learnable samplers largely optimize for global representativeness, yet still neglect the critical preservation of fine-grained edge structures and localized geometric details. To address these issues, we propose SE-PCS, a task-agnostic sampling framework that combines edge-aware geometric priors with semantic constraints from self-supervised vision models. Our approach explicitly emphasizes high-curvature regions to retain structural details, while a DINOv3-driven semantic consistency loss encourages the sampled subset to capture discriminative features across tasks. Experiments on ModelNet40 and ShapeNet Core55 show that SE-PCS consistently surpasses classical and learning-based baselines in classification, retrieval, and completion. Remarkably, it achieves near-Oracle accuracy with only 256 points, demonstrating both efficiency and adaptability. These results highlight the effectiveness of integrating geometric cues with semantic guidance to obtain compact yet informative point cloud representations.