A new method combines noisy point generation, multi-scale feature extraction, and implicit surface discrimination to learn signed distance functions that detect anomalies in 3D point clouds, reporting AUROC gains of 2.1% and 3.6% on two benchmarks.
arXiv:2508.01311 [cs.CV] https://arxiv.org/abs/2508
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Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection
A new method combines noisy point generation, multi-scale feature extraction, and implicit surface discrimination to learn signed distance functions that detect anomalies in 3D point clouds, reporting AUROC gains of 2.1% and 3.6% on two benchmarks.