mlsanityData to 7 Oct 2026

MC3D-AD: A Unified Geometry-aware Reconstruction Model for Multi-category 3D Anomaly Detection

arXiv 2505.01969 ↗First posted 4 May 2025cs.CVcited by 23

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Abstract (arXiv)

3D Anomaly Detection (AD) is a promising means of controlling the quality of manufactured products. However, existing methods typically require carefully training a task-specific model for each category independently, leading to high cost, low efficiency, and weak generalization. Therefore, this paper presents a novel unified model for Multi-Category 3D Anomaly Detection (MC3D-AD) that aims to utilize both local and global geometry-aware information to reconstruct normal representations of all categories. First, to learn robust and generalized features of different categories, we propose an adaptive geometry-aware masked attention module that extracts geometry variation information to guide mask attention. Then, we introduce a local geometry-aware encoder reinforced by the improved mask attention to encode group-level feature tokens. Finally, we design a global query decoder that utilizes point cloud position embeddings to improve the decoding process and reconstruction ability. This leads to local and global geometry-aware reconstructed feature tokens for the AD task. MC3D-AD is evaluated on two publicly available Real3D-AD and Anomaly-ShapeNet datasets, and exhibits significant superiority over current state-of-the-art single-category methods, achieving 3.1% and 9.3% improvement in object-level AUROC over Real3D-AD and Anomaly-ShapeNet, respectively. The code is available at https://github.com/iCAN-SZU/MC3D-AD.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Builds on 21 of its 26 references that are in the map. First posted on arXiv 4 May 2025.

Builds on

21 of 26 references.

References · 21

  1. 2017Attention Is All You Needcited by 59,932
  2. 2021Towards Total Recall in Industrial Anomaly Detectioncited by 772
  3. 2021DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectioncited by 510
  4. 2022Masked Autoencoders for Point Cloud Self-supervised Learningcited by 456
  5. 2022A Unified Model for Multi-class Anomaly Detectioncited by 267
  6. 2021The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localizationcited by 174
  7. 2022DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detectioncited by 153
  8. 2023Multimodal Industrial Anomaly Detection via Hybrid Fusioncited by 138
  9. 2013Generalized Denoising Auto-Encoders as Generative Modelscited by 113
  10. 2022Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detectioncited by 92
  11. 2023Real3D-AD: A Dataset of Point Cloud Anomaly Detectioncited by 87
  12. 2023Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detectioncited by 74
9 more
  1. 2023Towards Scalable 3D Anomaly Detection and Localization: A Benchmark via 3D Anomaly Synthesis and A Self-Supervised Learning Networkcited by 67
  2. 2022Anomaly Detection in 3D Point Clouds using Deep Geometric Descriptorscited by 66
  3. 2023Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detectioncited by 66
  4. 2024R3D-AD: Reconstruction via Diffusion for 3D Anomaly Detectioncited by 45
  5. 2024PO3AD: Predicting Point Offsets toward Better 3D Point Cloud Anomaly Detectioncited by 30
  6. 2024Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detectioncited by 30
  7. 2024PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly Detectioncited by 29
  8. 2024Towards High-resolution 3D Anomaly Detection via Group-Level Feature Contrastive Learningcited by 25
  9. 2021Multi-Class Anomaly Detectioncited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 7; 2026: 16.

23 papers.

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  1. 2025Towards High-Resolution 3D Anomaly Detection: A Scalable Dataset and Real-Time Framework for Subtle Industrial Defectscited by 15
  2. 2025A Lightweight 3D Anomaly Detection Method with Rotationally Invariant Featurescited by 5
  3. 2026Towards an Incremental Unified Multimodal Anomaly Detection: Augmenting Multimodal Denoising From an Information Bottleneck Perspectivecited by 4
  4. 2025C3D-AD: Toward Continual 3D Anomaly Detection via Kernel Attention with Learnable Advisorcited by 3
  5. 2025IEC3D-AD: A 3D Dataset of Industrial Equipment Components for Unsupervised Point Cloud Anomaly Detectioncited by 3
  6. 2026A Semantically Disentangled Unified Model for Multi-category 3D Anomaly Detectioncited by 3
  7. 20253DKeyAD: High-Resolution 3D Point Cloud Anomaly Detection via Keypoint-Guided Point Clusteringcited by 2
  8. 2025A Comprehensive Survey for Real-World Industrial Defect Detection: Challenges, Approaches, and Prospectscited by 2
  9. 2026DMP-3DAD: Cross-Category 3D Anomaly Detection via Realistic Depth Map Projection with Few Normal Samplescited by 1
  10. 2026Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detectioncited by 1
  11. 2026IMMoE: Incomplete Multi-View Anomaly Detection via Mixture of View Experts Fusioncited by 1
  12. 2025Physic-HM: Restoring Physical Generative Logic in Multimodal Anomaly Detection via Hierarchical Modulationcited by 0
11 more
  1. 2026Open-Set Supervised 3D Anomaly Detection: An Industrial Dataset and a Generalisable Framework for Unknown Defectscited by 0
  2. 2026Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detectioncited by 0
  3. 2026Breaking the Rigid Prior: Towards Articulated 3D Anomaly Detectioncited by 0
  4. 2026Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detectioncited by 0
  5. 2026CoGeoAD: Hierarchical Color-Geometric Fusion with Multi-View Attention for Zero-Shot 3D Anomaly Detectioncited by 0
  6. 2026Point Cloud Diffusion with Global and Local Reconstruction for Instance-Level 3D Anomaly Detectioncited by 0
  7. 2026M2P-AD: Memory-to-Prototype Learning with Boundary-aware Score Refinement for 3D Anomaly Detectioncited by 0
  8. 2026Understanding and Overcoming Cross-modal Fusion Bias in Multimodal Anomaly Detection From A Fisher Information Perspectivecited by 0
  9. 2026PC2-AD: Point Cloud Upsampling to Safeguard 3D Anomaly Detection with Resolution-constrained Edge Devicescited by 0
  10. 2026Beyond Geometry: Benchmarking and Consistency Reasoning for 3D Logical Anomaly Detectioncited by 0
  11. 2026Towards Generalizable 3D Anomaly Detection via Relational Inconsistency Modelingcited by 0