{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T16:44:15Z","timestamp":1774716255888,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":44,"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:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.U21A20518, No.U23A20341"],"award-info":[{"award-number":["No.U21A20518, No.U23A20341"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680578","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:41Z","timestamp":1729925981000},"page":"1505-1513","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["MambaMOS: LiDAR-based 3D Moving Object Segmentation with Motion-aware State Space Model"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8168-8676","authenticated-orcid":false,"given":"Kang","family":"Zeng","sequence":"first","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0184-2245","authenticated-orcid":false,"given":"Hao","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Optical Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1393-5027","authenticated-orcid":false,"given":"Jiacheng","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering, Hunan University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5467-7426","authenticated-orcid":false,"given":"Siyu","family":"Li","sequence":"additional","affiliation":[{"name":"School of Robotics, Hunan University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0121-4162","authenticated-orcid":false,"given":"Jintao","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, South China Normal University, Foshan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8272-3119","authenticated-orcid":false,"given":"Kaiwei","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Optical Science and Engineering, Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9720-5915","authenticated-orcid":false,"given":"Zhiyong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Robotics, Hunan University, Changsha, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1090-667X","authenticated-orcid":false,"given":"Kailun","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Robotics, Hunan University, Changsha, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Arjovsky Martin","year":"2016","unstructured":"Martin Arjovsky, Amar Shah, and Yoshua Bengio. 2016. Unitary evolution recurrent neural networks. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ECMR50962.2021.9568799"},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV).","author":"Behley Jens","year":"2020","unstructured":"Jens Behley, Martin Garbade, Andres Milioto, Jan Quenzel, Sven Behnke, Cyrill Stachniss, and J\u00fcrgen Gall. 2020. SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)."},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Berman Maxim","year":"2018","unstructured":"Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko. 2018. The lov\u00e1sz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3093567"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3166544"},{"key":"e_1_3_2_1_7_1","volume-title":"SuMa: Efficient LiDAR-based Semantic SLAM. In 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS).","author":"Chen Xieyuanli","year":"2019","unstructured":"Xieyuanli Chen, Andres Milioto, Emanuele Palazzolo, Philippe Giguere, Jens Behley, and Cyrill Stachniss. 2019. SuMa: Efficient LiDAR-based Semantic SLAM. In 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS)."},{"key":"e_1_3_2_1_8_1","volume-title":"MF-MOS: A Motion-Focused Model for Moving Object Segmentation. In 2024 IEEE International Conference on Robotics and Automation (ICRA).","author":"Cheng Jintao","year":"2024","unstructured":"Jintao Cheng, Kang Zeng, Zhuoxu Huang, Xiaoyu Tang, Jin Wu, Chengxi Zhang, Xieyuanli Chen, and Rui Fan. 2024. MF-MOS: A Motion-Focused Model for Moving Object Segmentation. In 2024 IEEE International Conference on Robotics and Automation (ICRA)."},{"key":"e_1_3_2_1_9_1","volume-title":"Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds. In International Symposium on Visual Computing (ISVC).","author":"Cortinhal Tiago","year":"2020","unstructured":"Tiago Cortinhal, George Tzelepis, and Eren Erdal Aksoy. 2020. SalsaNext: Fast, Uncertainty-Aware Semantic Segmentation of LiDAR Point Clouds. In International Symposium on Visual Computing (ISVC)."},{"key":"e_1_3_2_1_10_1","volume-title":"Flashattention: Fast and memory-efficient exact attention with io-awareness. Advances in Neural Information Processing Systems (NeurIPS)","author":"Dao Tri","year":"2022","unstructured":"Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christopher R\u00e9. 2022. Flashattention: Fast and memory-efficient exact attention with io-awareness. Advances in Neural Information Processing Systems (NeurIPS) (2022)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00291"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913491297"},{"key":"e_1_3_2_1_14_1","volume-title":"Modeling Sequences with Structured State Spaces","author":"Albert Gu.","unstructured":"Albert Gu. 2023. Modeling Sequences with Structured State Spaces. Stanford University."},{"key":"e_1_3_2_1_15_1","volume-title":"Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752","author":"Gu Albert","year":"2023","unstructured":"Albert Gu and Tri Dao. 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752 (2023)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-38452-7"},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Hua Weizhe","year":"2022","unstructured":"Weizhe Hua, Zihang Dai, Hanxiao Liu, and Quoc Le. 2022. Transformer quality in linear time. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS45743.2020.9340856"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3186080"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00028"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00831"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2023.103488"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jag.2023.103507"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3061363"},{"key":"e_1_3_2_1_25_1","unstructured":"HyungTae Lim Lucas Nunes Benedikt Mersch Xieyunali Chen Jens Behley Hyun Myung and Cyrill Stachniss. 2023. ERASOR2: Instance-aware robust 3D mapping of the static world in dynamic scenes. In Robotics: Science and Systems (RSS)."},{"key":"e_1_3_2_1_26_1","volume-title":"Decoupled Weight Decay Regularization. In International Conference on Learning Representations (ICLR).","author":"Loshchilov Ilya","year":"2018","unstructured":"Ilya Loshchilov and Frank Hutter. 2018. Decoupled Weight Decay Regularization. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2022.3183245"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3292583"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967762"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.5220\/0010866000003124"},{"key":"e_1_3_2_1_31_1","unstructured":"Guy M Morton. 1966. A computer oriented geodetic data base and a new technique in file sequencing. (1966)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48506.2021.9560730"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2801797"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3305239"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2024.3353835","article-title":"SSF-MOS: Semantic Scene Flow Assisted Moving Object Segmentation for Autonomous Vehicles","volume":"73","author":"Song Tao","year":"2024","unstructured":"Tao Song, Yunhao Liu, Ziying Yao, and Xinkai Wu. 2024. SSF-MOS: Semantic Scene Flow Assisted Moving Object Segmentation for Autonomous Vehicles. IEEE Transactions on Instrumentation and Measurement (TIM), Vol. 73 (2024), 1--12.","journal-title":"IEEE Transactions on Instrumentation and Measurement (TIM)"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9981210"},{"key":"e_1_3_2_1_38_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N. Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems (NeurIPS)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS55552.2023.10342277"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3618331"},{"key":"e_1_3_2_1_41_1","volume-title":"Stronger. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR).","author":"Wu Xiaoyang","year":"2024","unstructured":"Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. 2024. Point Transformer V3: Simpler, Faster, Stronger. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_1_42_1","volume-title":"Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding. arXiv preprint arXiv:2304.06906","author":"Yang Yu-Qi","year":"2023","unstructured":"Yu-Qi Yang, Yu-Xiao Guo, Jian-Yu Xiong, Yang Liu, Hao Pan, Peng-Shuai Wang, Xin Tong, and Baining Guo. 2023. Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding. arXiv preprint arXiv:2304.06906 (2023)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00962"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2023.3325687"}],"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\/3664647.3680578","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680578","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:56Z","timestamp":1750295876000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680578"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":44,"alternative-id":["10.1145\/3664647.3680578","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680578","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"}}]}}