Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects

• URL: http://arxiv.org/abs/2604.01171v1
• Date: Wed, 01 Apr 2026 17:23:23 GMT
• Title: Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defects
• Abstract summary: Self-supervised 3D anomaly detection assumes that acquiring high-precision point clouds is computationally expensive.<n>We study open-set supervised 3D anomaly detection, where the model is trained with only normal samples and a small number of known anomalous samples.<n>We present Open-Industry, a high-quality industrial dataset containing 15 categories, each with five real anomaly types collected from production lines.
• Score: 19.312310628271806
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although self-supervised 3D anomaly detection assumes that acquiring high-precision point clouds is computationally expensive, in real manufacturing scenarios it is often feasible to collect a limited number of anomalous samples. Therefore, we study open-set supervised 3D anomaly detection, where the model is trained with only normal samples and a small number of known anomalous samples, aiming to identify unknown anomalies at test time. We present Open-Industry, a high-quality industrial dataset containing 15 categories, each with five real anomaly types collected from production lines. We first adapt general open-set anomaly detection methods to accommodate 3D point cloud inputs better. Building upon this, we propose Open3D-AD, a point-cloud-oriented approach that leverages normal samples, simulated anomalies, and partially observed real anomalies to model the probability density distributions of normal and anomalous data. Then, we introduce a simple Correspondence Distributions Subsampling to reduce the overlap between normal and non-normal distributions, enabling stronger dual distributions modeling. Based on these contributions, we establish a comprehensive benchmark and evaluate the proposed method extensively on Open-Industry as well as established datasets including Real3D-AD and Anomaly-ShapeNet. Benchmark results and ablation studies demonstrate the effectiveness of Open3D-AD and further reveal the potential of open-set supervised 3D anomaly detection.

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