Batched Point Cloud Anomaly Detection via Structural Position-aware Comparison

Point Cloud Anomaly Detection (PCAD) is critical for industrial inspection. We propose Structural position-aware point cloud Anomaly Detection (SpotAD), the novel batched PCAD framework that performs anomaly detection directly on point cloud. Our SpotAD enables position-aware comparison through deterministic PCA-based alignment. To ensure both efficiency and structural consistency, we introduce object-aware prototype selection to restrict comparisons to structurally similar objects, position-aware prototype filtering for geometrically constrained comparison, and local prototype score smoothing to propagate prototype-level anomaly scores to local neighborhoods, enforcing spatial continuity. This strategy preserving geometric correspondence. Extensive evaluations on four benchmark datasets demonstrate state-of-the-art performance under the batched PCAD setting, achieving 87.0% object-level AUROC and 86.3% point-level AUROC, while outperforming existing zero-shot and full-shot PCAD methods.