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

• URL: http://arxiv.org/abs/2507.07838v1
• Date: Thu, 10 Jul 2025 15:09:20 GMT
• Title: 3D-ADAM: A Dataset for 3D Anomaly Detection in Advanced Manufacturing
• Authors: Paul McHard, Florent P. Audonnet, Oliver Summerell, Sebastian Andraos, Paul Henderson, Gerardo Aragon-Camarasa,
• Abstract summary: 3D-ADAM is the first large-scale industry-relevant dataset for high-precision 3D Anomaly Detection.<n>It comprises 14,120 high-resolution scans across 217 unique parts, captured using 4 industrial depth imaging sensors.<n>It includes 27,346 annotated defect instances from 12 categories, covering the breadth of industrial surface defects.
• Score: 5.096333816641487
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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