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.

翻译：尽管自监督三维异常检测假设获取高精度点云的计算成本高昂，但在实际制造场景中，收集有限数量的异常样本往往是可行的。为此，我们研究开放集监督式三维异常检测——模型仅使用正常样本与少量已知异常样本进行训练，旨在测试阶段识别未知异常。我们提出Open-Industry高质量工业数据集，包含15个类别，每个类别含五种从产线采集的真实异常类型。首先适配通用开放集异常检测方法以更好适应三维点云输入。在此基础上，我们提出面向点云的Open3D-AD方法，通过利用正常样本、模拟异常和部分观测真实异常来建模正常与异常数据的概率密度分布。随后引入简洁的对应分布子采样策略，降低正常分布与非正常分布的重叠程度，实现更强效的双分布建模。基于上述贡献，我们建立综合基准，并在Open-Industry及Real3D-AD、Anomaly-ShapeNet等既有数据集上对提出方法进行广泛评估。基准测试与消融实验证明了Open3D-AD的有效性，并进一步揭示了开放集监督式三维异常检测的潜力。