PointSimAD: Training-free Zero-shot 3D Anomaly Detection via Intrinsic Self-similarity

Zero-shot 3D anomaly detection (ZS-3DAD) aims to detect and localize anomalies in unseen object categories without requiring target-category training samples, making it particularly attractive for data-scarce industrial inspection. However, existing ZS-3DAD methods often rely on external auxiliary data to adapt pretrained models to the industrial domain, making their performance sensitive to the distribution of such data. In this paper, we propose PointSimAD, a training-free framework for ZS-3DAD that requires no fine-tuning with external industrial data and directly exploits the intrinsic structure of target point clouds. Specifically, we introduce a structural fingerprint that encodes local geometry, global shape, and spatial distribution into a sample-level descriptor. Based on this structural fingerprint, a structure-aware point grouping module adaptively determines the local structural granularity and partitions the point cloud into local point sets. A frozen 3D encoder then encodes the resulting local point sets into patch-level feature representations. By comparing each patch feature with similar features within the same object, we assess intra-object structural consistency and detect and localize anomalies based on the resulting local inconsistency. Experiments on three 3D anomaly detection benchmarks demonstrate that PointSimAD achieves strong and consistent performance, particularly for point-level anomaly localization, while requiring substantially lower memory and computation than existing adaptation-based approaches. The source code will be made publicly available upon acceptance.