Recently, 3D anomaly detection, a crucial problem involving fine-grained
geometry discrimination, is getting more attention. However, the lack of
abundant real 3D anomaly data limits the scalability of current models. To
enable scalable anomaly data collection, we propose a 3D anomaly synthesis
pipeline to adapt existing large-scale 3Dmodels for 3D anomaly detection.
Specifically, we construct a synthetic dataset, i.e., Anomaly-ShapeNet, basedon
ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40
categories, which provides a rich and varied collection of data, enabling
efficient training and enhancing adaptability to industrial scenarios.
Meanwhile,to enable scalable representation learning for 3D anomaly
localization, we propose a self-supervised method, i.e., Iterative Mask
Reconstruction Network (IMRNet). During training, we propose a geometry-aware
sample module to preserve potentially anomalous local regions during point
cloud down-sampling. Then, we randomly mask out point patches and sent the
visible patches to a transformer for reconstruction-based self-supervision.
During testing, the point cloud repeatedly goes through the Mask Reconstruction
Network, with each iteration's output becoming the next input. By merging and
contrasting the final reconstructed point cloud with the initial input, our
method successfully locates anomalies. Experiments show that IMRNet outperforms
previous state-of-the-art methods, achieving 66.1% in I-AUC on Anomaly-ShapeNet
dataset and 72.5% in I-AUC on Real3D-AD dataset. Our dataset will be released
at this https URL

为了解决现有模型的可扩展性问题，本文提出了一种基于现有大规模 3D 模型的 3D 异常合成流程，以适应 3D 异常检测。同时，为了实现可扩展的 3D 异常定位表示学习，本文提出了一种自我监督方法 —— 迭代掩蔽重建网络（IMRNet），并在 Anomaly-ShapeNet 数据集和 Real3D-AD 数据集上取得了显著优于先前方法的成果。

面向可扩展的三维异常检测和定位：通过三维异常合成和自我监督学习网络的基准测试

Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via  3D Anomaly Synthesis and A Self-Supervised Learning Network

3D anomaly detection is an emerging and vital computer vision task in
industrial manufacturing (IM). Recently many advanced algorithms have been
published, but most of them cannot meet the needs of IM. There are several
disadvantages: i) difficult to deploy on production lines since their
algorithms heavily rely on large pre-trained models; ii) hugely increase
storage overhead due to overuse of memory banks; iii) the inference speed
cannot be achieved in real-time. To overcome these issues, we propose an easy
and deployment-friendly network (called EasyNet) without using pre-trained
models and memory banks: firstly, we design a multi-scale multi-modality
feature encoder-decoder to accurately reconstruct the segmentation maps of
anomalous regions and encourage the interaction between RGB images and depth
images; secondly, we adopt a multi-modality anomaly segmentation network to
achieve a precise anomaly map; thirdly, we propose an attention-based
information entropy fusion module for feature fusion during inference, making
it suitable for real-time deployment. Extensive experiments show that EasyNet
achieves an anomaly detection AUROC of 92.6% without using pre-trained models
and memory banks. In addition, EasyNet is faster than existing methods, with a
high frame rate of 94.55 FPS on a Tesla V100 GPU.

3D 异常检测是工业制造中的一项新兴和重要的计算机视觉任务。本文提出了一种易于部署的网络（称为 EasyNet），可以在不使用预训练模型和存储器过多的情况下实现准确的异常检测，同时具有高效的推理速度。实验结果表明，EasyNet 在不使用预训练模型和存储器的情况下，实现了 92.6% 的异常检测 AUROC，并且比现有方法更快，Tesla V100 GPU 上的帧率达到了 94.55 FPS。