mlsanityData to 14 Oct 2026

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

arXiv 2507.07838 ↗First posted 10 Jul 2025cs.CVcited by 1

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Abstract (arXiv)

Surface defects are a primary source of yield loss in manufacturing, yet existing anomaly detection methods often fail in real-world deployment due to limited and unrepresentative datasets. To overcome this, we introduce 3D-ADAM, a 3D Anomaly Detection in Additive Manufacturing dataset, that is the first large-scale, industry-relevant dataset for RGB+3D surface defect detection in additive manufacturing. 3D-ADAM comprises 14,120 high-resolution scans of 217 unique parts, captured with four industrial depth sensors, and includes 27,346 annotated defects across 12 categories along with 27,346 annotations of machine element features in 16 classes. 3D-ADAM is captured in a real industrial environment and as such reflects real production conditions, including variations in part placement, sensor positioning, lighting, and partial occlusion. Benchmarking state-of-the-art models demonstrates that 3D-ADAM presents substantial challenges beyond existing datasets. Validation through expert labelling surveys with industry partners further confirms its industrial relevance. By providing this benchmark, 3D-ADAM establishes a foundation for advancing robust 3D anomaly detection capable of meeting manufacturing demands.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 22 of its 34 references that are in the map. First posted on arXiv 10 Jul 2025.

Builds on

22 of 34 references.

References · 22

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  3. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  5. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 776
  6. 2020PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and Localizationcited by 594
  7. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  8. 2023AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detectioncited by 271
  9. 2023Putting the Object Back into Video Object Segmentationcited by 188
  10. 2016Thingi10K: A Dataset of 10,000 3D-Printing Modelscited by 183
  11. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 178
  12. 2022CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly Localizationcited by 135
10 more
  1. 2022DSR -- A dual subspace re-projection network for surface anomaly detectioncited by 90
  2. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  3. 2022Anomalib: A Deep Learning Library for Anomaly Detectioncited by 75
  4. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 75
  5. 2022The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localizationcited by 57
  6. 2023PAD: A Dataset and Benchmark for Pose-agnostic Anomaly Detectioncited by 46
  7. 2023TransFusion -- A Transparency-Based Diffusion Model for Anomaly Detectioncited by 43
  8. 2023Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth Simulationcited by 33
  9. 2025Boosting Global-Local Feature Matching via Anomaly Synthesis for Multi-Class Point Cloud Anomaly Detectioncited by 16
  10. 2024ContextFlow++: Generalist-Specialist Flow-based Generative Models with Mixed-Variable Context Encodingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Is Task-Specific Training Necessary for Anomaly Detection?cited by 2