3D-ADAM: A Dataset for 3D Anomaly Detection in Advanced Manufacturing
Journal:
arXiv
Published Date:
Jul 10, 2025
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.
