{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:51:44Z","timestamp":1777654304847,"version":"3.51.4"},"publisher-location":"Cham","reference-count":63,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031734137","type":"print"},{"value":"9783031734144","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,25]],"date-time":"2024-10-25T00:00:00Z","timestamp":1729814400000},"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\/978-3-031-73414-4_25","type":"book-chapter","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T17:02:54Z","timestamp":1729789374000},"page":"434-452","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["MesonGS: Post-training Compression of\u00a03D Gaussians via\u00a0Efficient Attribute Transformation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3017-1077","authenticated-orcid":false,"given":"Shuzhao","family":"Xie","sequence":"first","affiliation":[]},{"given":"Weixiang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Chen","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Yunpeng","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Rongwei","family":"Lu","sequence":"additional","affiliation":[]},{"given":"Shijia","family":"Ge","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5462-6178","authenticated-orcid":false,"given":"Zhi","family":"Wang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,10,25]]},"reference":[{"key":"25_CR1","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":"25_CR2","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-NeRF: anti-aliased grid-based neural radiance fields. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01804"},{"key":"25_CR3","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation (2013). https:\/\/arxiv.org\/abs\/1308.3432"},{"key":"25_CR4","doi-asserted-by":"publisher","unstructured":"Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: TensoRF: tensorial radiance fields. In: Avidan, S., Brostow, G.J., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXII. Lecture Notes in Computer Science, vol. 13692, pp. 333\u2013350. Springer (2022). https:\/\/doi.org\/10.1007\/978-3-031-19824-3_20","DOI":"10.1007\/978-3-031-19824-3_20"},{"key":"25_CR5","unstructured":"Chen, A., Xu, Z., Wei, X., Tang, S., Su, H., Geiger, A.: Factor fields: a unified framework for neural fields and beyond (2023). https:\/\/arxiv.org\/abs\/2302.01226"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, Z., Song, L., Chen, L., Yu, J., Yuan, J., Xu, Y.: NeuRBF: a neural fields representation with adaptive radial basis functions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4182\u20134194 (2023)","DOI":"10.1109\/ICCV51070.2023.00386"},{"key":"25_CR7","doi-asserted-by":"publisher","unstructured":"Chen, Z., Funkhouser, T.A., Hedman, P., Tagliasacchi, A.: MobileNeRF: exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023, pp. 16569\u201316578. IEEE (2023). https:\/\/doi.org\/10.1109\/CVPR52729.2023.01590","DOI":"10.1109\/CVPR52729.2023.01590"},{"issue":"8","key":"25_CR8","doi-asserted-by":"publisher","first-page":"3947","DOI":"10.1109\/TIP.2016.2575005","volume":"25","author":"RL De Queiroz","year":"2016","unstructured":"De Queiroz, R.L., Chou, P.A.: Compression of 3D point clouds using a region-adaptive hierarchical transform. IEEE Trans. Image Process. 25(8), 3947\u20133956 (2016)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Deng, C.L., Tartaglione, E.: Compressing explicit voxel grid representations: fast nerfs become also small. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1236\u20131245 (2023)","DOI":"10.1109\/WACV56688.2023.00129"},{"issue":"10","key":"25_CR10","doi-asserted-by":"publisher","first-page":"1568","DOI":"10.1109\/29.35395","volume":"37","author":"WH Equitz","year":"1989","unstructured":"Equitz, W.H.: A new vector quantization clustering algorithm. IEEE Trans. Acoust. Speech Signal Process. 37(10), 1568\u20131575 (1989)","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"25_CR11","unstructured":"Fan, Z., Wang, K., Wen, K., Zhu, Z., Xu, D., Wang, Z.: LightGaussian: unbounded 3D gaussian compression with 15x reduction and 200+ FPS. arXiv preprint arXiv:2311.17245 (2023)"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Fang, G., Hu, Q., Wang, H., Xu, Y., Guo, Y.: 3DAC: learning attribute compression for point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14819\u201314828 (2022)","DOI":"10.1109\/CVPR52688.2022.01440"},{"key":"25_CR13","unstructured":"Fang, G., Hu, Q., Wang, L., Guo, Y.: ACRF: Compressing explicit neural radiance fields via attribute compression. In: International Conference on Learning Representations(ICLR) (2024)"},{"key":"25_CR14","unstructured":"Frantar, E., Ashkboos, S., Hoefler, T., Alistarh, D.: GPTQ: accurate post-training compression for generative pretrained transformers. In: ICLR (2023)"},{"key":"25_CR15","doi-asserted-by":"publisher","unstructured":"Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: radiance fields without neural networks. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pp. 5491\u20135500. IEEE (2022). https:\/\/doi.org\/10.1109\/CVPR52688.2022.00542","DOI":"10.1109\/CVPR52688.2022.00542"},{"key":"25_CR16","unstructured":"Gailly, J.l., Adler, M.: ZLIB general purpose compression library. user manual zlib version 1(4) (2003)"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Girish, S., Gupta, K., Shrivastava, A.: Eagles: efficient accelerated 3D gaussians with lightweight encodings. arXiv preprint arXiv:2312.04564 (2023)","DOI":"10.1007\/978-3-031-73036-8_4"},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Girish, S., Shrivastava, A., Gupta, K.: SHACIRA: scalable hash-grid compression for implicit neural representations. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 17513\u201317524 (2023)","DOI":"10.1109\/ICCV51070.2023.01606"},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Hedman, P., et al.: Deep blending for free-viewpoint image-based rendering. ToG (2018)","DOI":"10.1145\/3272127.3275084"},{"key":"25_CR20","doi-asserted-by":"publisher","unstructured":"Hedman, P., Srinivasan, P.P., Mildenhall, B., Barron, J.T., Debevec, P.E.: Baking neural radiance fields for real-time view synthesis. In: 2021 IEEE\/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10-17, 2021, pp. 5855\u20135864. IEEE (2021). https:\/\/doi.org\/10.1109\/ICCV48922.2021.00582","DOI":"10.1109\/ICCV48922.2021.00582"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Hu, W., et al.: Tri-MipRF: tri-Mip representation for efficient anti-aliasing neural radiance fields. In: ICCV (2023)","DOI":"10.1109\/ICCV51070.2023.01811"},{"issue":"9","key":"25_CR22","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1109\/JRPROC.1952.273898","volume":"40","author":"DA Huffman","year":"1952","unstructured":"Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proc. IRE 40(9), 1098\u20131101 (1952)","journal-title":"Proc. IRE"},{"issue":"4","key":"25_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3592433","volume":"42","author":"B Kerbl","year":"2023","unstructured":"Kerbl, B., Kopanas, G., Leimk\u00fchler, T., Drettakis, G.: 3D gaussian splatting for real-time radiance field rendering. ACM Trans. Graph. (ToG) 42(4), 1\u201314 (2023)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"25_CR24","doi-asserted-by":"crossref","unstructured":"Knapitsch, A., et al. Tanks and temples: benchmarking large-scale scene reconstruction. ToG (2017)","DOI":"10.1145\/3072959.3073599"},{"key":"25_CR25","doi-asserted-by":"crossref","unstructured":"Lee, J.C., Rho, D., Sun, X., Ko, J.H., Park, E.: Compact 3D gaussian representation for radiance field. arXiv preprint arXiv:2311.13681 (2023)","DOI":"10.1109\/CVPR52733.2024.02052"},{"key":"25_CR26","doi-asserted-by":"crossref","unstructured":"Li, L., Shen, Z., Wang, Z., Shen, L., Bo, L.: Compressing volumetric radiance fields to 1 mb. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4222\u20134231 (2023)","DOI":"10.1109\/CVPR52729.2023.00411"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Mahmoud, O., Ladune, T., Gendrin, M.: CAwa-NeRF: instant learning of compression-aware nerf features. arXiv preprint arXiv:2310.14695 (2023)","DOI":"10.1109\/SDS60720.2024.00015"},{"issue":"2","key":"25_CR28","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/0146-664X(82)90104-6","volume":"19","author":"D Meagher","year":"1982","unstructured":"Meagher, D.: Geometric modeling using octree encoding. Comput. Graph. Image Process. 19(2), 129\u2013147 (1982)","journal-title":"Comput. Graph. Image Process."},{"issue":"1","key":"25_CR29","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":"25_CR30","doi-asserted-by":"crossref","unstructured":"Morgenstern, W., Barthel, F., Hilsmann, A., Eisert, P.: Compact 3D scene representation via self-organizing gaussian grids. arXiv preprint arXiv:2312.13299 (2023)","DOI":"10.1007\/978-3-031-73013-9_2"},{"issue":"4","key":"25_CR31","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. Graph. (ToG) 41(4), 1\u201315 (2022)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"25_CR32","unstructured":"Navaneet, K., Meibodi, K.P., Koohpayegani, S.A., Pirsiavash, H.: Compact3D: compressing gaussian splat radiance field models with vector quantization. arXiv preprint arXiv:2311.18159 (2023)"},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Niedermayr, S., Stumpfegger, J., Westermann, R.: Compressed 3D gaussian splatting for accelerated novel view synthesis. In: CVPR (2024)","DOI":"10.1109\/CVPR52733.2024.00985"},{"key":"25_CR34","doi-asserted-by":"crossref","unstructured":"Papantonakis, P., Kopanas, G., Kerbl, B., Lanvin, A., Drettakis, G.: Reducing the memory footprint of 3D gaussian splatting. Proc. ACM Comput. Graph. Interact. Tech. 7(1), 1\u201317 (2024). https:\/\/repo-sam.inria.fr\/fungraph\/reduced-3dgs\/","DOI":"10.1145\/3651282"},{"key":"25_CR35","unstructured":"Pranckevi\u010dius, A.: https:\/\/aras-p.info\/blog\/2023\/09\/13\/Making-Gaussian-Splats-smaller\/ (2023). Accessed 28 Oct 2023"},{"key":"25_CR36","unstructured":"Pranckevi\u010dius, A.: https:\/\/aras-p.info\/blog\/2023\/09\/27\/Making-Gaussian-Splats-more-smaller\/ (2023). Accessed 28 Oct 2023"},{"key":"25_CR37","doi-asserted-by":"crossref","unstructured":"Qin, M., Li, W., Zhou, J., Wang, H., Pfister, H.: LangSplat: 3D language gaussian splatting. arXiv preprint arXiv:2312.16084 (2023)","DOI":"10.1109\/CVPR52733.2024.01895"},{"key":"25_CR38","doi-asserted-by":"publisher","unstructured":"Reiser, C., et al.: MERF: memory-efficient radiance fields for real-time view synthesis in unbounded scenes. ACM Trans. Graph. 42(4), 1\u201312 (2023). https:\/\/doi.org\/10.1145\/3592426","DOI":"10.1145\/3592426"},{"key":"25_CR39","doi-asserted-by":"crossref","unstructured":"Rho, D., Lee, B., Nam, S., Lee, J.C., Ko, J.H., Park, E.: Masked wavelet representation for compact neural radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20680\u201320690 (June 2023)","DOI":"10.1109\/CVPR52729.2023.01981"},{"key":"25_CR40","doi-asserted-by":"crossref","unstructured":"Richardson, I.E.: H. 264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia. John Wiley & Sons (2004)","DOI":"10.1002\/0470869615"},{"issue":"1","key":"25_CR41","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1109\/JETCAS.2018.2885981","volume":"9","author":"S Schwarz","year":"2018","unstructured":"Schwarz, S., Preda, M., Baroncini, V., Budagavi, M., Cesar, P., Chou, P.A., Cohen, R.A., Krivoku\u0107a, M., Lasserre, S., Li, Z., et al.: Emerging mpeg standards for point cloud compression. IEEE J. Emerg. Sel. Top. Circuits Syst. 9(1), 133\u2013148 (2018)","journal-title":"IEEE J. Emerg. Sel. Top. Circuits Syst."},{"key":"25_CR42","doi-asserted-by":"publisher","unstructured":"Sculley, D.: Web-scale k-means clustering. In: Proceedings of the 19th International Conference on World Wide Web. pp. 1177\u20131178. Association for Computing Machinery, New York, USA (2010). https:\/\/doi.org\/10.1145\/1772690.1772862","DOI":"10.1145\/1772690.1772862"},{"key":"25_CR43","unstructured":"Shin, S., Park, J.: Binary radiance fields. arXiv preprint arXiv:2306.07581 (2023)"},{"key":"25_CR44","doi-asserted-by":"crossref","unstructured":"Song, R., Fu, C., Liu, S., Li, G.: Efficient hierarchical entropy model for learned point cloud compression. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14368\u201314377 (2023)","DOI":"10.1109\/CVPR52729.2023.01381"},{"key":"25_CR45","doi-asserted-by":"crossref","unstructured":"Sun, C., Sun, M., Chen, H.T.: Direct voxel grid optimization: super-fast convergence for radiance fields reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5459\u20135469 (2022)","DOI":"10.1109\/CVPR52688.2022.00538"},{"key":"25_CR46","doi-asserted-by":"publisher","unstructured":"Takikawa, T., et al.: Variable bitrate neural fields. In: Nandigjav, M., Mitra, N.J., Hertzmann, A. (eds.) SIGGRAPH: special Interest Group on Computer Graphics and Interactive Techniques Conference, Vancouver, BC, Canada, pp. 1\u20139. ACM (2022). https:\/\/doi.org\/10.1145\/3528233.3530727","DOI":"10.1145\/3528233.3530727"},{"key":"25_CR47","doi-asserted-by":"crossref","unstructured":"Tang, C., et al.: Mixed-precision neural network quantization via learned layer-wise importance. In: European Conference on Computer Vision (2022)","DOI":"10.1007\/978-3-031-20083-0_16"},{"key":"25_CR48","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Neural residual radiance fields for streamably free-viewpoint videos. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 76\u201387 (2023)","DOI":"10.1109\/CVPR52729.2023.00016"},{"issue":"4","key":"25_CR49","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1109\/TIP.2003.819861","volume":"13","author":"Z Wang","year":"2004","unstructured":"Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600\u2013612 (2004)","journal-title":"IEEE Trans. Image Process."},{"key":"25_CR50","doi-asserted-by":"publisher","unstructured":"Wei, X., Zhang, Y., Li, Y., Zhang, X., Gong, R., Guo, J., Liu, X.: Outlier suppression+: accurate quantization of large language models by equivalent and effective shifting and scaling. In: Bouamor, H., Pino, J., Bali, K. (eds.) Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 1648\u20131665. Association for Computational Linguistics, Singapore (December 2023). https:\/\/doi.org\/10.18653\/v1\/2023.emnlp-main.102, https:\/\/aclanthology.org\/2023.emnlp-main.102","DOI":"10.18653\/v1\/2023.emnlp-main.102"},{"issue":"8","key":"25_CR51","doi-asserted-by":"publisher","first-page":"1309","DOI":"10.1109\/83.855427","volume":"9","author":"MJ Weinberger","year":"2000","unstructured":"Weinberger, M.J., Seroussi, G., Sapiro, G.: The loco-i lossless image compression algorithm: principles and standardization into jpeg-ls. IEEE Trans. Image Process. 9(8), 1309\u20131324 (2000)","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"25_CR52","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1145\/214762.214771","volume":"30","author":"IH Witten","year":"1987","unstructured":"Witten, I.H., Neal, R.M., Cleary, J.G.: Arithmetic coding for data compression. Commun. ACM 30(6), 520\u2013540 (1987)","journal-title":"Commun. ACM"},{"key":"25_CR53","doi-asserted-by":"crossref","unstructured":"Xu, Q., et al.: Point-nerf: point-based neural radiance fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5438\u20135448 (2022)","DOI":"10.1109\/CVPR52688.2022.00536"},{"issue":"6","key":"25_CR54","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3355089.3356513","volume":"38","author":"W Yifan","year":"2019","unstructured":"Yifan, W., Serena, F., Wu, S., \u00d6ztireli, C., Sorkine-Hornung, O.: Differentiable surface splatting for point-based geometry processing. ACM Trans. Graph. (TOG) 38(6), 1\u201314 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"25_CR55","doi-asserted-by":"crossref","unstructured":"Zhang, C., Florencio, D., Loop, C.: Point cloud attribute compression with graph transform. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 2066\u20132070. IEEE (2014)","DOI":"10.1109\/ICIP.2014.7025414"},{"key":"25_CR56","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 586\u2013595 (2018)","DOI":"10.1109\/CVPR.2018.00068"},{"key":"25_CR57","doi-asserted-by":"crossref","unstructured":"Zhao, T., Chen, J., Leng, C., Cheng, J.: TinyNeRF: towards 100 x compression of voxel radiance fields. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 3588\u20133596 (2023)","DOI":"10.1609\/aaai.v37i3.25469"},{"key":"25_CR58","unstructured":"Zhao, Y., et al.: Atom: low-bit quantization for efficient and accurate LLM serving. In: Gibbons, P., Pekhimenko, G., Sa, C.D. (eds.) Proceedings of Machine Learning and Systems, vol.\u00a06, pp. 196\u2013209 (2024). https:\/\/proceedings.mlsys.org\/paper_files\/paper\/2024\/file\/5edb57c05c81d04beb716ef1d542fe9e-Paper-Conference.pdf"},{"issue":"3","key":"25_CR59","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/TIT.1977.1055714","volume":"23","author":"J Ziv","year":"1977","unstructured":"Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Trans. Inf. Theory 23(3), 337\u2013343 (1977)","journal-title":"IEEE Trans. Inf. Theory"},{"issue":"5","key":"25_CR60","doi-asserted-by":"publisher","first-page":"530","DOI":"10.1109\/TIT.1978.1055934","volume":"24","author":"J Ziv","year":"1978","unstructured":"Ziv, J., Lempel, A.: Compression of individual sequences via variable-rate coding. IEEE Trans. Inf. Theory 24(5), 530\u2013536 (1978)","journal-title":"IEEE Trans. Inf. Theory"},{"key":"25_CR61","doi-asserted-by":"publisher","unstructured":"Zwicker, M., Pfister, H., van Baar, J., Gross, M.: EWA volume splatting. In: Proceedings Visualization, 2001. VIS \u201901, pp. 29\u2013538 (2001). https:\/\/doi.org\/10.1109\/VISUAL.2001.964490","DOI":"10.1109\/VISUAL.2001.964490"},{"key":"25_CR62","doi-asserted-by":"crossref","unstructured":", Zwicker, M., Pfister, H., Van\u00a0Baar, J., Gross, M.: Surface splatting. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 371\u2013378 (2001)","DOI":"10.1145\/383259.383300"},{"key":"25_CR63","unstructured":"\u010cerven\u00fd, J.: https:\/\/gsplat.tech (2023). Accessed 28 Oct 2023"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73414-4_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,30]],"date-time":"2024-11-30T06:48:59Z","timestamp":1732949339000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73414-4_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,25]]},"ISBN":["9783031734137","9783031734144"],"references-count":63,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73414-4_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,25]]},"assertion":[{"value":"25 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","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":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}