首个融合多视角多模态的3D异常检测基准，支持从合成数据泛化到真实场景。
SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark
- 构建多视角多模态数据集，融合高分辨率图像与点云信息。
- 在8类共333个物体上验证，支持单实例训练与真实数据泛化。
- 提供3D异常分割标注，适配工业质检与少样本学习研究者。
我们提出SiM3D，首个整合多视角与多模态信息的3D异常检测与分割（ADS）基准，任务目标为生成体素级异常体积。该基准聚焦制造业关键场景：单实例异常检测，即仅用一个真实或合成对象进行训练。在此背景下，SiM3D是首个解决从合成数据训练到真实测试数据泛化的挑战的ADS基准。SiM3D包含使用顶级工业传感器和机器人采集的新颖多模态多视角数据集，涵盖333个物体实例，分属8种类型，每类配有CAD模型。数据集包含多视角高分辨率图像（12 Mpx）与点云（700万点），并提供异常测试样本的手动3D分割真值标注。为建立新提出的多视角3D ADS任务基线，我们适配了主流单视角方法，并采用新型基于异常体积的评估指标进行性能分析。
原文摘要 · Abstract (English)
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.
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