Training-Free 3D Anomaly Detection via Viewpoint-Preserving Alignment and Residual Calibration

3D anomaly detection aims to identify defective objects and localize structural deviations from a small set of normal observations. Existing methods often detect anomalies indirectly through learned or engineered representations, or through discrepancies from reconstructed normal geometry. For structural anomalies, however, the anomaly signal is inherently geometric, suggesting a simpler and direct alternative: align a query point cloud with normal references and directly measure their geometric residuals. The challenge is that large residuals are not unique to defects; they can also arise from alignment errors, partial observations, and sampling noise. In this work, we argue that geometric residuals become meaningful anomaly cues when pose-induced discrepancies are controlled and residual evidence is evaluated consistently across spatial scales. Motivated by this principle, we introduce MARS, a training-free 3D anomaly-detection method that addresses the residual ambiguity issues through viewpoint-preserving Multi-hypothesis Alignment and normal-calibrated Residual Scoring. MARS first generates candidate poses from precomputed reference views and preserves alternative viewing directions during hypothesis screening before selecting the final pose. Then, query-to-normal residuals are summarized across multiple spatial scales and evaluated against the corresponding residual statistics observed when normal references are compared with one another. With only a few normal references per category and no pretrained models, training, or synthesized anomalies, MARS achieves 97.0%/91.2% point-/object-level AUROC on Real3D-AD and 98.2%/95.7% on Anomaly-ShapeNet, outperforming recent methods on average.