{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T11:30:44Z","timestamp":1764588644658,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":28,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819665983","type":"print"},{"value":"9789819665969","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-96-6596-9_28","type":"book-chapter","created":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:30:57Z","timestamp":1749274257000},"page":"398-412","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning Segmented 3D Gaussians via\u00a0Efficient Feature Unprojection for\u00a0Zero-Shot Neural Scene Segmentation"],"prefix":"10.1007","author":[{"given":"Bin","family":"Dou","sequence":"first","affiliation":[]},{"given":"Tianyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Zhaohui","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Yongjia","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Zejian","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Nanning","family":"Zheng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,8]]},"reference":[{"issue":"1","key":"28_CR1","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1145\/3503250","volume":"65","author":"B Mildenhall","year":"2021","unstructured":"Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: NERF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99\u2013106 (2021)","journal-title":"Commun. ACM"},{"key":"28_CR2","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: MIP-Nerf 360: unbounded anti-aliased neural radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5470\u20135479 (2022)","DOI":"10.1109\/CVPR52688.2022.00539"},{"key":"28_CR3","doi-asserted-by":"crossref","unstructured":"Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: radiance fields without neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5501\u20135510 (2022)","DOI":"10.1109\/CVPR52688.2022.00542"},{"key":"28_CR4","doi-asserted-by":"crossref","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3D gaussian splatting for real-time radiance field rendering. ACM Trans. Graphics 42(4) (2023)","DOI":"10.1145\/3592433"},{"key":"28_CR5","unstructured":"Wang, B., Chen, L., Yang, B.: DM-Nerf: 3D scene geometry decomposition and manipulation from 2D images. arXiv preprint arXiv:2208.07227 (2022)"},{"key":"28_CR6","doi-asserted-by":"crossref","unstructured":"Zhi, S., Laidlow, T., Leutenegger, S., Davison, A.J.: In-place scene labelling and understanding with implicit scene representation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15838\u201315847 (2021)","DOI":"10.1109\/ICCV48922.2021.01554"},{"key":"28_CR7","doi-asserted-by":"crossref","unstructured":"Siddiqui, Y., et al.: Panoptic lifting for 3D scene understanding with neural fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9043\u20139052 (2023)","DOI":"10.1109\/CVPR52729.2023.00873"},{"key":"28_CR8","doi-asserted-by":"crossref","unstructured":"Ye, M., Danelljan, M., Yu, F., Ke, L.: Gaussian grouping: Segment and edit anything in 3d scenes. arXiv preprint arXiv:2312.00732 (2023)","DOI":"10.1007\/978-3-031-73397-0_10"},{"key":"28_CR9","first-page":"23311","volume":"35","author":"S Kobayashi","year":"2022","unstructured":"Kobayashi, S., Matsumoto, E., Sitzmann, V.: Decomposing nerf for editing via feature field distillation. Adv. Neural. Inf. Process. Syst. 35, 23311\u201323330 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR10","doi-asserted-by":"crossref","unstructured":"Zhou, S., et al.: Feature 3DGS: supercharging 3D Gaussian splatting to enable distilled feature fields. arXiv preprint arXiv:2312.03203 (2023)","DOI":"10.1109\/CVPR52733.2024.02048"},{"key":"28_CR11","doi-asserted-by":"crossref","unstructured":"Caron, M., et al.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9650\u20139660 (2021)","DOI":"10.1109\/ICCV48922.2021.00951"},{"key":"28_CR12","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., Girdhar, R.: Masked-attention mask transformer for universal image segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 1290\u20131299 (2022)","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"28_CR13","unstructured":"Kirillov, A., et\u00a0al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)"},{"key":"28_CR14","doi-asserted-by":"crossref","unstructured":"Tang, S., Pei, W., Tao, X., Jia, T., Lu, G., Tai, Y.W.: Scene-generalizable interactive segmentation of radiance fields. In: Proceedings of the 31st ACM International Conference on Multimedia, pp. 6744\u20136755 (2023)","DOI":"10.1145\/3581783.3612246"},{"key":"28_CR15","unstructured":"Li, B., Weinberger, K.Q., Belongie, S., Koltun, V., Ranftl, R.: Language-driven semantic segmentation. arXiv preprint arXiv:2201.03546 (2022)"},{"key":"28_CR16","unstructured":"Zhang, K., Riegler, G., Snavely, N., Koltun, V.: Nerf++: analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.07492 (2020)"},{"issue":"4","key":"28_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3528223.3530127","volume":"41","author":"T M\u00fcller","year":"2022","unstructured":"M\u00fcller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graphics (ToG) 41(4), 1\u201315 (2022)","journal-title":"ACM Trans. Graphics (ToG)"},{"key":"28_CR18","doi-asserted-by":"crossref","unstructured":"Fu, X., et al.: Panoptic Nerf: 3D-to-2D label transfer for panoptic urban scene segmentation. In: 2022 International Conference on 3D Vision (3DV), pp. 1\u201311. IEEE (2022)","DOI":"10.1109\/3DV57658.2022.00042"},{"key":"28_CR19","unstructured":"Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"28_CR20","doi-asserted-by":"crossref","unstructured":"Landrieu, L., Simonovsky, M.: Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4558\u20134567 (2018)","DOI":"10.1109\/CVPR.2018.00479"},{"key":"28_CR21","doi-asserted-by":"crossref","unstructured":"Hu, Q., et al.: RandLA-Net: efficient semantic segmentation of large-scale point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11108\u201311117 (2020)","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"28_CR22","unstructured":"Ma, X., Huang, H., Wang, Y., Romano, S., Erfani, S., Bailey, J.: Normalized loss functions for deep learning with noisy labels. In: International Conference on Machine Learning, pp. 6543\u20136553. PMLR (2020)"},{"key":"28_CR23","first-page":"30284","volume":"34","author":"E Englesson","year":"2021","unstructured":"Englesson, E., Azizpour, H.: Generalized Jensen-Shannon divergence loss for learning with noisy labels. Adv. Neural. Inf. Process. Syst. 34, 30284\u201330297 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"28_CR24","doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Gaussianeditor: swift and controllable 3D editing with gaussian splatting. arXiv preprint arXiv:2311.14521 (2023)","DOI":"10.1109\/CVPR52733.2024.02029"},{"key":"28_CR25","unstructured":"Straub, J., et\u00a0al.: The replica dataset: a digital replica of indoor spaces. arXiv preprint arXiv:1906.05797 (2019)"},{"key":"28_CR26","doi-asserted-by":"crossref","unstructured":"Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nie\u00dfner, M.: Scannet: richly-annotated 3D reconstructions of indoor scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5828\u20135839 (2017)","DOI":"10.1109\/CVPR.2017.261"},{"key":"28_CR27","doi-asserted-by":"crossref","unstructured":"Li, F., et al.: Mask DINO: towards a unified transformer-based framework for object detection and segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3041\u20133050 (2023)","DOI":"10.1109\/CVPR52729.2023.00297"},{"key":"28_CR28","doi-asserted-by":"crossref","unstructured":"Cheng, H.K., Oh, S.W., Price, B., Schwing, A., Lee, J.Y.: Tracking anything with decoupled video segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 1316\u20131326 (2023)","DOI":"10.1109\/ICCV51070.2023.00127"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6596-9_28","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T05:31:11Z","timestamp":1749274271000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6596-9_28"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819665983","9789819665969"],"references-count":28,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6596-9_28","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"8 June 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}