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However, 2D images limit the improvement of anomaly detection accuracy without utilizing depth information. Therefore, this paper proposes a <jats:bold>D<\/jats:bold>ual <jats:bold>R<\/jats:bold>econstruction vi<jats:bold>A<\/jats:bold><jats:bold>I<\/jats:bold>npainting <jats:bold>N<\/jats:bold>etwork for 3D industrial anomaly detection (<jats:bold>DRAIN<\/jats:bold>). Firstly, we design a 3D reconstruction network using an encoder-decoder-based U-shaped network for processing RGB images and depth images. Subsequently, accurate anomaly segmentation is implemented through a 3D segmentation network. We introduce a lightweight MLP module to enhance segmentation performance to capture long-range dependencies in the reconstructed images. Furthermore, we propose a dual attention-based information entropy fusion module to expedite feature fusion in the inference process, aiming for enhanced deployment in the industry. Extensive experiments demonstrate that DRAIN achieves a 94.3% AUROC on the 3D anomaly detection dataset MVTec 3D-AD, surpassing other research methods.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical abstract<\/jats:title>\n                <jats:p>Overall architecture for 3D industrial anomaly detection via dual reconstruction network<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s10489-024-05700-x","type":"journal-article","created":{"date-parts":[[2024,7,30]],"date-time":"2024-07-30T10:02:15Z","timestamp":1722333735000},"page":"9956-9970","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["3D Industrial anomaly detection via dual reconstruction network"],"prefix":"10.1007","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4554-6018","authenticated-orcid":false,"given":"Zhuo","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3479-9802","authenticated-orcid":false,"given":"Yifei","family":"Ge","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1251-9040","authenticated-orcid":false,"given":"Xin","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4351-6923","authenticated-orcid":false,"given":"Lin","family":"Meng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,30]]},"reference":[{"key":"5700_CR1","doi-asserted-by":"crossref","unstructured":"Bergmann P, Fauser M, Sattlegger D, Steger C (2020) Uninformed students: student-teacher anomaly detection with discriminative latent embeddings. 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