MFF-M3AD: A unified reconstruction method with multi-scale feature fusion for multi-category 3D anomaly detection
- DOI
- 10.1016/j.neunet.2026.109131
- Published
- 2026-11
- Container
- Neural Networks
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109131,
title = {MFF-M3AD: A unified reconstruction method with multi-scale feature fusion for multi-category 3D anomaly detection},
author = {Hanzhe Liang and Chenxi Hu and Yejin Tang and Linlin Shen and Jinbao Wang and Can Gao},
year = {2026},
journal = {Neural Networks},
doi = {10.1016/j.neunet.2026.109131},
url = {https://doi.org/10.1016/j.neunet.2026.109131}
}RIS
TY - JOUR TI - MFF-M3AD: A unified reconstruction method with multi-scale feature fusion for multi-category 3D anomaly detection AU - Hanzhe Liang AU - Chenxi Hu AU - Yejin Tang AU - Linlin Shen AU - Jinbao Wang AU - Can Gao PY - 2026 JO - Neural Networks DO - 10.1016/j.neunet.2026.109131 UR - https://doi.org/10.1016/j.neunet.2026.109131 ER -
APA
Liang, H., Hu, C., Tang, Y., Shen, L., Wang, J., & Gao, C. (2026). MFF-M3AD: A unified reconstruction method with multi-scale feature fusion for multi-category 3D anomaly detection. Neural Networks. https://doi.org/10.1016/j.neunet.2026.109131
Source records
- crossref · retrieved 2026-09-26T07:03:00.909Z