Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916698493288448 |
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| author | Zhu, Wenbing Wang, Lidong Zhou, Ziqing Wang, Chengjie Pan, Yurui Zhang, Ruoyi Chen, Zhuhao Cheng, Linjie Gao, Bin-Bin Zhang, Jiangning Gan, Zhenye Wang, Yuxie Chen, Yulong Qian, Shuguang Chi, Mingmin Peng, Bo Ma, Lizhuang |
| author_facet | Zhu, Wenbing Wang, Lidong Zhou, Ziqing Wang, Chengjie Pan, Yurui Zhang, Ruoyi Chen, Zhuhao Cheng, Linjie Gao, Bin-Bin Zhang, Jiangning Gan, Zhenye Wang, Yuxie Chen, Yulong Qian, Shuguang Chi, Mingmin Peng, Bo Ma, Lizhuang |
| contents | The increasing complexity of industrial anomaly detection (IAD) has positioned multimodal detection methods as a focal area of machine vision research. However, dedicated multimodal datasets specifically tailored for IAD remain limited. Pioneering datasets like MVTec 3D have laid essential groundwork in multimodal IAD by incorporating RGB+3D data, but still face challenges in bridging the gap with real industrial environments due to limitations in scale and resolution. To address these challenges, we introduce Real-IAD D3, a high-precision multimodal dataset that uniquely incorporates an additional pseudo3D modality generated through photometric stereo, alongside high-resolution RGB images and micrometer-level 3D point clouds. Real-IAD D3 features finer defects, diverse anomalies, and greater scale across 20 categories, providing a challenging benchmark for multimodal IAD Additionally, we introduce an effective approach that integrates RGB, point cloud, and pseudo-3D depth information to leverage the complementary strengths of each modality, enhancing detection performance. Our experiments highlight the importance of these modalities in boosting detection robustness and overall IAD performance. The dataset and code are publicly accessible for research purposes at https://realiad4ad.github.io/Real-IAD D3 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14221 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection Zhu, Wenbing Wang, Lidong Zhou, Ziqing Wang, Chengjie Pan, Yurui Zhang, Ruoyi Chen, Zhuhao Cheng, Linjie Gao, Bin-Bin Zhang, Jiangning Gan, Zhenye Wang, Yuxie Chen, Yulong Qian, Shuguang Chi, Mingmin Peng, Bo Ma, Lizhuang Computer Vision and Pattern Recognition The increasing complexity of industrial anomaly detection (IAD) has positioned multimodal detection methods as a focal area of machine vision research. However, dedicated multimodal datasets specifically tailored for IAD remain limited. Pioneering datasets like MVTec 3D have laid essential groundwork in multimodal IAD by incorporating RGB+3D data, but still face challenges in bridging the gap with real industrial environments due to limitations in scale and resolution. To address these challenges, we introduce Real-IAD D3, a high-precision multimodal dataset that uniquely incorporates an additional pseudo3D modality generated through photometric stereo, alongside high-resolution RGB images and micrometer-level 3D point clouds. Real-IAD D3 features finer defects, diverse anomalies, and greater scale across 20 categories, providing a challenging benchmark for multimodal IAD Additionally, we introduce an effective approach that integrates RGB, point cloud, and pseudo-3D depth information to leverage the complementary strengths of each modality, enhancing detection performance. Our experiments highlight the importance of these modalities in boosting detection robustness and overall IAD performance. The dataset and code are publicly accessible for research purposes at https://realiad4ad.github.io/Real-IAD D3 |
| title | Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.14221 |