SiM3D：单实例多视图多模态多设置3D异常检测基准
SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark
AI总结：
SiM3D是首个集成多视图与多模态信息的3D异常检测基准，针对单实例训练场景，支持从合成数据泛化到真实测试，并提供新数据集与基线评估方法。
摘要：
我们提出了SiM3D，这是第一个考虑多视图和多模态信息集成以进行全面3D异常检测与分割（ADS）的基准，其任务是生成基于体素的异常体积。此外，SiM3D聚焦于制造业中高度关注的场景：单实例异常检测，即仅有一个物体（无论是真实的还是合成的）可用于训练。在这方面，SiM3D作为第一个解决从合成训练数据泛化到真实测试数据这一挑战的ADS基准而脱颖而出。SiM3D包含一个使用顶级工业传感器和机器人采集的新型多模态多视图数据集。该数据集包含333个实例（涵盖八种物体类型）的多视图高分辨率图像（12 Mpx）和点云（7M点），以及每种类型的CAD模型。我们还为异常测试样本提供了手动标注的3D分割真值。为了为所提出的多视图3D ADS任务建立参考基线，我们改编了著名的单视图方法，并使用基于异常体积的新颖指标评估其性能。
英文摘要：
We propose SiM3D, the first benchmark considering the integration of multiview and multimodal information for comprehensive 3D anomaly detection and segmentation (ADS), where the task is to produce a voxel-based Anomaly Volume. Moreover, SiM3D focuses on a scenario of high interest in manufacturing: single-instance anomaly detection, where only one object, either real or synthetic, is available for training. In this respect, SiM3D stands out as the first ADS benchmark that addresses the challenge of generalising from synthetic training data to real test data. SiM3D includes a novel multimodal multiview dataset acquired using top-tier industrial sensors and robots. The dataset features multiview high-resolution images (12 Mpx) and point clouds (7M points) for 333 instances of eight types of objects, alongside a CAD model for each type. We also provide manually annotated 3D segmentation GTs for anomalous test samples. To establish reference baselines for the proposed multiview 3D ADS task, we adapt prominent singleview methods and assess their performance using novel metrics that operate on Anomaly Volumes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
