SiM3D: Single-instance Multiview Multimodal and Multisetup 3D Anomaly Detection Benchmark
This addresses the problem of comprehensive 3D anomaly detection in manufacturing, particularly for scenarios with limited training data, but it is incremental as it builds on existing methods by adapting them to a new benchmark.
The paper introduces SiM3D, a benchmark for 3D anomaly detection and segmentation that integrates multiview and multimodal data, focusing on single-instance scenarios where only one object is available for training, and it includes a dataset with 333 instances and provides baseline results using adapted methods.
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.