{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T16:25:13Z","timestamp":1775579113422,"version":"3.50.1"},"publisher-location":"Cham","reference-count":70,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031734106","type":"print"},{"value":"9783031734113","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"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\/978-3-031-73411-3_19","type":"book-chapter","created":{"date-parts":[[2024,11,22]],"date-time":"2024-11-22T20:07:53Z","timestamp":1732306073000},"page":"330-349","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["CompGS: Smaller and\u00a0Faster Gaussian Splatting with\u00a0Vector Quantization"],"prefix":"10.1007","author":[{"given":"K L","family":"Navaneet","sequence":"first","affiliation":[]},{"given":"Kossar","family":"Pourahmadi Meibodi","sequence":"additional","affiliation":[]},{"given":"Soroush","family":"Abbasi Koohpayegani","sequence":"additional","affiliation":[]},{"given":"Hamed","family":"Pirsiavash","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,23]]},"reference":[{"key":"19_CR1","unstructured":"Official code repository of 3d gaussian splatting for real-time radiance field rendering. https:\/\/github.com\/graphdeco-inria\/gaussian-splatting"},{"key":"19_CR2","first-page":"12980","volume":"33","author":"S Abbasi Koohpayegani","year":"2020","unstructured":"Abbasi Koohpayegani, S., Tejankar, A., Pirsiavash, H.: Compress: self-supervised learning by compressing representations. In: Advances in Neural Information Processing Systems, vol. 33, pp. 33, 12980\u201312992 (2020)","journal-title":"In: Advances in Neural Information Processing Systems, vol. 33, pp."},{"key":"19_CR3","unstructured":"Ba, L.J., Caruana, R.: Do deep nets really need to be deep? arXiv preprint arXiv:1312.6184 (2013)"},{"key":"19_CR4","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":"19_CR5","unstructured":"Baruch, G., et al.: Arkitscenes - a diverse real-world dataset for 3D indoor scene understanding using mobile RGB-D data. In: NeurIPS (2021). https:\/\/arxiv.org\/pdf\/2111.08897.pdf"},{"key":"19_CR6","unstructured":"Baruch, G., et al.: ARKitscenes: a diverse real-world dataset for 3D indoor scene understanding using mobile RGB-d data. In: Thirty-Fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 1) (2021). https:\/\/openreview.net\/forum?id=tjZjv_qh_CE"},{"key":"19_CR7","unstructured":"Bengio, Y., L\u00e9onard, N., Courville, A.: Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)"},{"key":"19_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/978-3-031-19824-3_20","volume-title":"Computer Vision - ECCV 2022","author":"A Chen","year":"2022","unstructured":"Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: Tensorf: tensorial radiance fields. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) ECCV 2022. LNCS, vol. 13692, pp. 333\u2013350. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19824-3_20"},{"key":"19_CR9","unstructured":"Chen, G., Choi, W., Yu, X., Han, T., Chandraker, M.: Learning efficient object detection models with knowledge distillation. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 742\u2013751 (2017)"},{"key":"19_CR10","doi-asserted-by":"crossref","unstructured":"Chen, Z., Funkhouser, T., Hedman, P., Tagliasacchi, A.: MobileNerf: exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16569\u201316578 (2023)","DOI":"10.1109\/CVPR52729.2023.01590"},{"key":"19_CR11","unstructured":"Cho, M., et al.: EDKM: an efficient and accurate train-time weight clustering for large language models. arXiv preprint arXiv:2309.00964 (2023)"},{"issue":"9","key":"19_CR12","doi-asserted-by":"publisher","first-page":"1326","DOI":"10.1109\/5.237540","volume":"81","author":"PC Cosman","year":"1993","unstructured":"Cosman, P.C., Oehler, K.L., Riskin, E.A., Gray, R.M.: Using vector quantization for image processing. Proc. IEEE 81(9), 1326\u20131341 (1993)","journal-title":"Proc. IEEE"},{"key":"19_CR13","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"},{"key":"19_CR14","unstructured":"Dettmers, T., Lewis, M., Belkada, Y., Zettlemoyer, L.: Llm. int8 (): 8-bit matrix multiplication for transformers at scale. arXiv preprint arXiv:2208.07339 (2022)"},{"issue":"10","key":"19_CR15","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 Sig. Process. 37(10), 1568\u20131575 (1989)","journal-title":"IEEE Trans. Acoust. Speech Sig. Process."},{"key":"19_CR16","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":"19_CR17","doi-asserted-by":"crossref","unstructured":"Flynn, J., Neulander, I., Philbin, J., Snavely, N.: Deepstereo: learning to predict new views from the world\u2019s imagery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5515\u20135524 (2016)","DOI":"10.1109\/CVPR.2016.595"},{"key":"19_CR18","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":"19_CR19","doi-asserted-by":"crossref","unstructured":"Garbin, S.J., Kowalski, M., Johnson, M., Shotton, J., Valentin, J.: FastNerf: high-fidelity neural rendering at 200fps. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14346\u201314355 (2021)","DOI":"10.1109\/ICCV48922.2021.01408"},{"key":"19_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4615-3626-0","volume-title":"Vector Quantization and Signal Compression","author":"A Gersho","year":"2012","unstructured":"Gersho, A., Gray, R.M.: Vector Quantization and Signal Compression, vol. 159. Springer, New York (2012). https:\/\/doi.org\/10.1007\/978-1-4615-3626-0"},{"key":"19_CR21","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":"19_CR22","unstructured":"Gong, Y., Liu, L., Yang, M., Bourdev, L.: Compressing deep convolutional networks using vector quantization. arXiv preprint arXiv:1412.6115 (2014)"},{"issue":"2","key":"19_CR23","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/MASSP.1984.1162229","volume":"1","author":"R Gray","year":"1984","unstructured":"Gray, R.: Vector quantization. IEEE ASSP Mag. 1(2), 4\u201329 (1984)","journal-title":"IEEE ASSP Mag."},{"key":"19_CR24","doi-asserted-by":"crossref","unstructured":"Gu, S., et al.: Vector quantized diffusion model for text-to-image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10696\u201310706 (2022)","DOI":"10.1109\/CVPR52688.2022.01043"},{"key":"19_CR25","unstructured":"Gupta, S., Agrawal, A., Gopalakrishnan, K., Narayanan, P.: Deep learning with limited numerical precision. In: International Conference on Machine Learning, pp. 1737\u20131746. PMLR (2015)"},{"key":"19_CR26","unstructured":"Han, S., Mao, H., Dally, W.J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv preprint arXiv:1510.00149 (2015)"},{"issue":"6","key":"19_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3272127.3275084","volume":"37","author":"P Hedman","year":"2018","unstructured":"Hedman, P., Philip, J., Price, T., Frahm, J.M., Drettakis, G., Brostow, G.: Deep blending for free-viewpoint image-based rendering. ACM Trans. Graph. (ToG) 37(6), 1\u201315 (2018)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"19_CR28","doi-asserted-by":"crossref","unstructured":"Hedman, P., Srinivasan, P.P., Mildenhall, B., Barron, J.T., Debevec, P.: Baking neural radiance fields for real-time view synthesis. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5875\u20135884 (2021)","DOI":"10.1109\/ICCV48922.2021.00582"},{"key":"19_CR29","doi-asserted-by":"crossref","unstructured":"Henzler, P., Mitra, N.J., Ritschel, T.: Escaping Plato\u2019s cave: 3D shape from adversarial rendering. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9984\u20139993 (2019)","DOI":"10.1109\/ICCV.2019.01008"},{"key":"19_CR30","unstructured":"Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531 (2015)"},{"key":"19_CR31","unstructured":"Huang, B., Yan, X., Chen, A., Gao, S., Yu, J.: Pref: phasorial embedding fields for compact neural representations. arXiv preprint arXiv:2205.13524 (2022)"},{"key":"19_CR32","doi-asserted-by":"crossref","unstructured":"Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2704\u20132713 (2018)","DOI":"10.1109\/CVPR.2018.00286"},{"issue":"4","key":"19_CR33","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)"},{"issue":"4","key":"19_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3072959.3073599","volume":"36","author":"A Knapitsch","year":"2017","unstructured":"Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: benchmarking large-scale scene reconstruction. ACM Trans. Graph. (ToG) 36(4), 1\u201313 (2017)","journal-title":"ACM Trans. Graph. (ToG)"},{"key":"19_CR35","unstructured":"Krishnamoorthi, R.: Quantizing deep convolutional networks for efficient inference: a whitepaper. arXiv preprint arXiv:1806.08342 (2018)"},{"key":"19_CR36","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":"19_CR37","doi-asserted-by":"crossref","unstructured":"Lee, J.C., Rho, D., Sun, X., Ko, J.H., Park, E.: Compact 3D Gaussian representation for radiance field. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 21719\u201321728 (2024)","DOI":"10.1109\/CVPR52733.2024.02052"},{"issue":"3","key":"19_CR38","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1109\/83.366484","volume":"4","author":"YY Lee","year":"1995","unstructured":"Lee, Y.Y., Woods, J.W.: Motion vector quantization for video coding. IEEE Trans. Image Process. 4(3), 378\u2013382 (1995)","journal-title":"IEEE Trans. Image Process."},{"key":"19_CR39","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":"19_CR40","unstructured":"Li, L., Shen, Z., Wang, Z., Shen, L., Tan, P.: Streaming radiance fields for 3D video synthesis. In: Advances in Neural Information Processing Systems, vol. 35, pp. 13485\u201313498 (2022)"},{"key":"19_CR41","doi-asserted-by":"crossref","unstructured":"Ling, L., et\u00a0al.: Dl3dv-10k: a large-scale scene dataset for deep learning-based 3D vision. arXiv preprint arXiv:2312.16256 (2023)","DOI":"10.1109\/CVPR52733.2024.02092"},{"issue":"11","key":"19_CR42","doi-asserted-by":"publisher","first-page":"1551","DOI":"10.1109\/PROC.1985.13340","volume":"73","author":"J Makhoul","year":"1985","unstructured":"Makhoul, J., Roucos, S., Gish, H.: Vector quantization in speech coding. Proc. IEEE 73(11), 1551\u20131588 (1985)","journal-title":"Proc. IEEE"},{"key":"19_CR43","doi-asserted-by":"publisher","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. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 405\u2013421. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58452-8_24, http:\/\/arxiv.org\/abs\/2003.08934v2","DOI":"10.1007\/978-3-030-58452-8_24"},{"key":"19_CR44","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":"19_CR45","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":"19_CR46","doi-asserted-by":"crossref","unstructured":"Niedermayr, S., Stumpfegger, J., Westermann, R.: Compressed 3D Gaussian splatting for accelerated novel view synthesis. arXiv preprint arXiv:2401.02436 (2023)","DOI":"10.1109\/CVPR52733.2024.00985"},{"key":"19_CR47","doi-asserted-by":"crossref","unstructured":"Niedermayr, S., Stumpfegger, J., Westermann, R.: Compressed 3D Gaussian splatting for accelerated novel view synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10349\u201310358 (2024)","DOI":"10.1109\/CVPR52733.2024.00985"},{"key":"19_CR48","doi-asserted-by":"crossref","unstructured":"Nooralinejad, P., et al.: Pranc: pseudo random networks for compacting deep models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 17021\u201317031 (2023)","DOI":"10.1109\/ICCV51070.2023.01561"},{"key":"19_CR49","unstructured":"Peng, S., Jiang, C., Liao, Y., Niemeyer, M., Pollefeys, M., Geiger, A.: Shape as points: a differentiable Poisson solver. In: Advances in Neural Information Processing Systems, vol. 34, pp. 13032\u201313044 (2021)"},{"issue":"6","key":"19_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3130800.3130855","volume":"36","author":"E Penner","year":"2017","unstructured":"Penner, E., Zhang, L.: Soft 3D reconstruction for view synthesis. ACM Trans. Graph. (TOG) 36(6), 1\u201311 (2017)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"19_CR51","unstructured":"Polino, A., Pascanu, R., Alistarh, D.: Model compression via distillation and quantization. arXiv preprint arXiv:1802.05668 (2018)"},{"key":"19_CR52","doi-asserted-by":"crossref","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: Xnor-net: imagenet classification using binary convolutional neural networks (2016)","DOI":"10.1007\/978-3-319-46493-0_32"},{"key":"19_CR53","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/978-3-319-46493-0_32","volume-title":"Computer Vision \u2013 ECCV 2016","author":"M Rastegari","year":"2016","unstructured":"Rastegari, M., Ordonez, V., Redmon, J., Farhadi, A.: XNOR-net: imagenet classification using binary convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part IV. LNCS, vol. 9908, pp. 525\u2013542. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_32"},{"key":"19_CR54","unstructured":"Razavi, A., Van\u00a0den Oord, A., Vinyals, O.: Generating diverse high-fidelity images with VQ-VAE-2. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"19_CR55","doi-asserted-by":"crossref","unstructured":"Reiser, C., Peng, S., Liao, Y., Geiger, A.: KiloNerf: speeding up neural radiance fields with thousands of tiny MLPs. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14335\u201314345 (2021)","DOI":"10.1109\/ICCV48922.2021.01407"},{"key":"19_CR56","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1007\/978-3-030-58529-7_37","volume-title":"Computer Vision \u2013 ECCV 2020","author":"G Riegler","year":"2020","unstructured":"Riegler, G., Koltun, V.: Free view synthesis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part XIX. LNCS, vol. 12364, pp. 623\u2013640. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58529-7_37"},{"key":"19_CR57","unstructured":"Schwarz, K., Sauer, A., Niemeyer, M., Liao, Y., Geiger, A.: Voxgraf: fast 3D-aware image synthesis with sparse voxel grids. In: Advances in Neural Information Processing Systems, vol. 35, pp. 33999\u201334011 (2022)"},{"key":"19_CR58","doi-asserted-by":"crossref","unstructured":"Sitzmann, V., Thies, J., Heide, F., Nie\u00dfner, M., Wetzstein, G., Zollhofer, M.: Deepvoxels: learning persistent 3D feature embeddings. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2437\u20132446 (2019)","DOI":"10.1109\/CVPR.2019.00254"},{"key":"19_CR59","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":"19_CR60","doi-asserted-by":"crossref","unstructured":"Takikawa, T., et al.: Variable bitrate neural fields. In: ACM SIGGRAPH 2022 Conference Proceedings, pp.\u00a01\u20139 (2022)","DOI":"10.1145\/3528233.3530727"},{"key":"19_CR61","unstructured":"Tang, J., Chen, X., Wang, J., Zeng, G.: Compressible-composable nerf via rank-residual decomposition. In: Advances in Neural Information Processing Systems (2022)"},{"issue":"4","key":"19_CR62","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3306346.3323035","volume":"38","author":"J Thies","year":"2019","unstructured":"Thies, J., Zollh\u00f6fer, M., Nie\u00dfner, M.: Deferred neural rendering: image synthesis using neural textures. ACM Trans. Graph. (TOG) 38(4), 1\u201312 (2019)","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"19_CR63","unstructured":"Van Den\u00a0Oord, A., Vinyals, O., et\u00a0al.: Neural discrete representation learning. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"19_CR64","unstructured":"Vanhoucke, V., Senior, A., Mao, M.Z.: Improving the speed of neural networks on CPUs (2011)"},{"key":"19_CR65","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Fourier plenoctrees for dynamic radiance field rendering in real-time. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13524\u201313534 (2022)","DOI":"10.1109\/CVPR52688.2022.01316"},{"key":"19_CR66","unstructured":"Wen, W., Wu, C., Wang, Y., Chen, Y., Li, H.: Learning structured sparsity in deep neural networks. arXiv preprint arXiv:1608.03665 (2016)"},{"key":"19_CR67","doi-asserted-by":"crossref","unstructured":"Wu, X., et al.: Scalable neural indoor scene rendering. ACM Trans. Graph. (TOG) (2022)","DOI":"10.1145\/3528223.3530153"},{"key":"19_CR68","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"},{"key":"19_CR69","doi-asserted-by":"crossref","unstructured":"Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: Plenoctrees for real-time rendering of neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5752\u20135761 (2021)","DOI":"10.1109\/ICCV48922.2021.00570"},{"key":"19_CR70","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1007\/978-3-319-46493-0_18","volume-title":"Computer Vision \u2013 ECCV 2016","author":"T Zhou","year":"2016","unstructured":"Zhou, T., Tulsiani, S., Sun, W., Malik, J., Efros, A.A.: View synthesis by appearance flow. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016, Part IV. LNCS, vol. 9908, pp. 286\u2013301. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46493-0_18"}],"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\/978-3-031-73411-3_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T21:33:36Z","timestamp":1733088816000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73411-3_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,23]]},"ISBN":["9783031734106","9783031734113"],"references-count":70,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73411-3_19","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,23]]},"assertion":[{"value":"23 November 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"}}]}}