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

2026-04-01

AI Summary

This work addresses the challenge of detecting unknown-type defects in 3D point clouds from industrial manufacturing scenarios where only limited normal samples and a few known anomalies are available. To this end, we propose Open3D-AD, an open-set supervised 3D anomaly detection framework that jointly leverages normal data, synthetic anomalies, and a subset of real anomalies. Our approach models the probability density distributions of both positive (normal) and negative (anomalous) classes and introduces a distribution-aware subsampling strategy to enhance discriminability, complemented by a point-cloud-specific distribution disentanglement mechanism. We further contribute Open-Industry, the first high-quality dataset encompassing 15 categories of industrial products with five realistic defect types per category. Extensive experiments across multiple benchmarks demonstrate the effectiveness of our method, significantly advancing the state of the art in open-set 3D anomaly detection.

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.

Problem

• open-set anomaly detection
• 3D point cloud
• unknown defects
• industrial inspection
• anomaly detection

Innovation

• open-set anomaly detection
• 3D point cloud
• industrial dataset
• density estimation
• Correspondence Distributions Subsampling