{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:42:26Z","timestamp":1781584946647,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":28,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3681619","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:49Z","timestamp":1729925989000},"page":"10229-10237","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["IGSPAD: Inverting 3D Gaussian Splatting for Pose-agnostic Anomaly Detection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7138-7364","authenticated-orcid":false,"given":"Bolin","family":"Jiang","sequence":"first","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6425-0037","authenticated-orcid":false,"given":"Yuqiu","family":"Xie","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3873-8003","authenticated-orcid":false,"given":"Jiawei","family":"Li","sequence":"additional","affiliation":[{"name":"Huawei Manufacturing, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6472-0678","authenticated-orcid":false,"given":"Naiqi","family":"Li","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4798-230X","authenticated-orcid":false,"given":"Bin","family":"Chen","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology(Shenzhen), Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8639-982X","authenticated-orcid":false,"given":"Shu-Tao","family":"Xia","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00982"},{"key":"e_1_3_2_1_2_1","volume-title":"Segment any anomaly without training via hybrid prompt regularization. arXiv preprint arXiv:2305.10724","author":"Cao Yunkang","year":"2023","unstructured":"Yunkang Cao, Xiaohao Xu, Chen Sun, Yuqi Cheng, Zongwei Du, Liang Gao, and Weiming Shen. 2023. Segment any anomaly without training via hybrid prompt regularization. arXiv preprint arXiv:2305.10724 (2023)."},{"key":"e_1_3_2_1_3_1","volume-title":"Text-to-3d using gaussian splatting. arXiv preprint arXiv:2309.16585","author":"Chen Zilong","year":"2023","unstructured":"Zilong Chen, Feng Wang, and Huaping Liu. 2023. Text-to-3d using gaussian splatting. arXiv preprint arXiv:2309.16585 (2023)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-68799-1_35"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00188"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28696"},{"key":"e_1_3_2_1_8_1","volume-title":"13th International congress on ultra modern telecommunications and control systems and workshops (ICUMT). IEEE, 66--71","author":"Jezek Stepan","year":"2021","unstructured":"Stepan Jezek, Martin Jonak, Radim Burget, Pavel Dvorak, and Milos Skotak. 2021. Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions. In 2021 13th International congress on ultra modern telecommunications and control systems and workshops (ICUMT). IEEE, 66--71."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP48485.2024.10447663"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3592433"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3193699"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01616"},{"key":"e_1_3_2_1_14_1","volume-title":"Paul HJ Kelly, and Andrew J Davison","author":"Matsuki Hidenobu","year":"2023","unstructured":"Hidenobu Matsuki, Riku Murai, Paul HJ Kelly, and Andrew J Davison. 2023. Gaussian splatting slam. arXiv preprint arXiv:2312.06741 (2023)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIE45552.2021.9576231"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01392"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"e_1_3_2_1_19_1","volume-title":"d.]. Student-teacher feature pyramid matching for unsupervised anomaly detection. arXiv","author":"Wang G","year":"2021","unstructured":"G Wang, S Han, E Ding, and D Huang. [n. d.]. Student-teacher feature pyramid matching for unsupervised anomaly detection. arXiv 2021. arXiv preprint arXiv:2103.04257 1 ([n. d.])."},{"key":"e_1_3_2_1_20_1","volume-title":"Reconstruction student with attention for student-teacher pyramid matching. arXiv preprint arXiv:2111.15376","author":"Yamada Shinji","year":"2021","unstructured":"Shinji Yamada and Kazuhiro Hotta. 2021. Reconstruction student with attention for student-teacher pyramid matching. arXiv preprint arXiv:2111.15376 (2021)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS51168.2021.9636708"},{"key":"e_1_3_2_1_22_1","first-page":"4571","article-title":"A unified model for multi-class anomaly detection","volume":"35","author":"You Zhiyuan","year":"2022","unstructured":"Zhiyuan You, Lei Cui, Yujun Shen, Kai Yang, Xin Lu, Yu Zheng, and Xinyi Le. 2022. A unified model for multi-class anomaly detection. Advances in Neural Information Processing Systems 35 (2022), 4571--4584.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_23_1","volume-title":"Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677","author":"Yu Jiawei","year":"2021","unstructured":"Jiawei Yu, Ye Zheng, Xiang Wang, Wei Li, Yushuang Wu, Rui Zhao, and Liwei Wu. 2021. Fastflow: Unsupervised anomaly detection and localization via 2d normalizing flows. arXiv preprint arXiv:2111.07677 (2021)."},{"key":"e_1_3_2_1_24_1","volume-title":"Wide residual networks. arXiv preprint arXiv:1605.07146","author":"Zagoruyko Sergey","year":"2016","unstructured":"Sergey Zagoruyko and Nikos Komodakis. 2016. Wide residual networks. arXiv preprint arXiv:1605.07146 (2016)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00822"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00257"},{"key":"e_1_3_2_1_27_1","volume-title":"Pad: A dataset and benchmark for pose-agnostic anomaly detection. Advances in Neural Information Processing Systems 36","author":"Zhou Qiang","year":"2023","unstructured":"Qiang Zhou, Weize Li, Lihan Jiang, Guoliang Wang, Guyue Zhou, Shanghang Zhang, and Hao Zhao. 2023. Pad: A dataset and benchmark for pose-agnostic anomaly detection. Advances in Neural Information Processing Systems 36 (2023)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20056-4_23"}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","location":"Melbourne VIC Australia","acronym":"MM '24","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681619","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3681619","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:49Z","timestamp":1750295869000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3681619"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":28,"alternative-id":["10.1145\/3664647.3681619","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3681619","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}