Multi-View Reconstruction with Global Context for 3D 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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| Subjects: | |
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| _version_ | 1866908470905667584 |
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| author | Sun, Yihan Cheng, Yuqi Cao, Yunkang Zhang, Yuxin Shen, Weiming |
| author_facet | Sun, Yihan Cheng, Yuqi Cao, Yunkang Zhang, Yuxin Shen, Weiming |
| contents | 3D anomaly detection is critical in industrial quality inspection. While existing methods achieve notable progress, their performance degrades in high-precision 3D anomaly detection due to insufficient global information. To address this, we propose Multi-View Reconstruction (MVR), a method that losslessly converts high-resolution point clouds into multi-view images and employs a reconstruction-based anomaly detection framework to enhance global information learning. Extensive experiments demonstrate the effectiveness of MVR, achieving 89.6\% object-wise AU-ROC and 95.7\% point-wise AU-ROC on the Real3D-AD benchmark. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_21555 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-View Reconstruction with Global Context for 3D Anomaly Detection Sun, Yihan Cheng, Yuqi Cao, Yunkang Zhang, Yuxin Shen, Weiming Computer Vision and Pattern Recognition 3D anomaly detection is critical in industrial quality inspection. While existing methods achieve notable progress, their performance degrades in high-precision 3D anomaly detection due to insufficient global information. To address this, we propose Multi-View Reconstruction (MVR), a method that losslessly converts high-resolution point clouds into multi-view images and employs a reconstruction-based anomaly detection framework to enhance global information learning. Extensive experiments demonstrate the effectiveness of MVR, achieving 89.6\% object-wise AU-ROC and 95.7\% point-wise AU-ROC on the Real3D-AD benchmark. |
| title | Multi-View Reconstruction with Global Context for 3D Anomaly Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.21555 |