3D-ADAM: A Dataset for 3D Anomaly Detection in Advanced Manufacturing

2025-07-10

AI Summary

Industrial surface defect detection is hindered by scarcity of real-world data; existing RGB+3D industrial anomaly detection datasets are small-scale, low-precision, and suffer from scene distortion, limiting robust model development. Method: We introduce the first large-scale, high-accuracy RGB+3D surface defect dataset designed for industrial deployment. It encompasses 12 real-world defect types and 8 mechanical structural features, acquired via four industrial-grade depth sensors across 14,120 high-resolution scans, yielding 27,346 precisely annotated defect instances and 8,110 structural feature annotations. The dataset comprehensively models multi-source heterogeneous disturbances—including pose variation, complex illumination, and dynamic occlusion. Contribution/Results: Experiments show significant performance degradation of mainstream SOTA models, confirming its strong challenge. Domain experts validate its high quality and practical utility. This dataset establishes the first realistic, multimodal benchmark for industrial anomaly detection, bridging a critical gap in the field.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Surface defects are one of the largest contributors to low yield in the manufacturing sector. Accurate and reliable detection of defects during the manufacturing process is therefore of great value across the sector. State-of-the-art approaches to automated defect detection yield impressive performance on current datasets, yet still fall short in real-world manufacturing settings and developing improved methods relies on large datasets representative of real-world scenarios. Unfortunately, high-quality, high-precision RGB+3D industrial anomaly detection datasets are scarce, and typically do not reflect real-world industrial deployment scenarios. To address this, we introduce 3D-ADAM, the first large-scale industry-relevant dataset for high-precision 3D Anomaly Detection. 3D-ADAM comprises 14,120 high-resolution scans across 217 unique parts, captured using 4 industrial depth imaging sensors. It includes 27,346 annotated defect instances from 12 categories, covering the breadth of industrial surface defects. 3D-ADAM uniquely captures an additional 8,110 annotations of machine element features, spanning the range of relevant mechanical design form factors. Unlike existing datasets, 3D-ADAM is captured in a real industrial environment with variations in part position and orientation, camera positioning, ambient lighting conditions, as well as partial occlusions. Our evaluation of SOTA models across various RGB+3D anomaly detection tasks demonstrates the significant challenge this dataset presents to current approaches. We further validated the industrial relevance and quality of the dataset through an expert labelling survey conducted by industry partners. By providing this challenging benchmark, 3D-ADAM aims to accelerate the development of robust 3D Anomaly Detection models capable of meeting the demands of modern manufacturing environments.

Problem

• Lack of high-quality 3D industrial anomaly detection datasets
• Existing datasets fail to reflect real-world manufacturing scenarios
• Need for robust models to detect diverse surface defects

Innovation

• Large-scale high-precision 3D anomaly detection dataset
• Includes real industrial environment variations
• Covers 12 industrial surface defect categories