{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T18:26:25Z","timestamp":1776882385917,"version":"3.51.2"},"publisher-location":"Cham","reference-count":66,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031726729","type":"print"},{"value":"9783031726736","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T00:00:00Z","timestamp":1729555200000},"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\/978-3-031-72673-6_10","type":"book-chapter","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T16:03:50Z","timestamp":1729526630000},"page":"173-191","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Mesh2NeRF: Direct Mesh Supervision for\u00a0Neural Radiance Field Representation and\u00a0Generation"],"prefix":"10.1007","author":[{"given":"Yujin","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinyu","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin","family":"Ummenhofer","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reiner","family":"Birkl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael","family":"Paulitsch","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"M\u00fcller","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"Nie\u00dfner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,10,22]]},"reference":[{"key":"10_CR1","unstructured":"Poly haven models. https:\/\/polyhaven.com\/models"},{"key":"10_CR2","unstructured":"Skatchfab 3D models. https:\/\/sketchfab.com\/3d-models"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"\u00c1fra, A.T., Wald, I., Benthin, C., Woop, S.: Embree ray tracing kernels: overview and new features. In: SIGGRAPH Talks, pp. 52:1\u201352:2. ACM (2016)","DOI":"10.1145\/2897839.2927450"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Anciukevi\u010dius, T., et al.: Renderdiffusion: image diffusion for 3D reconstruction, inpainting and generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12608\u201312618 (2023)","DOI":"10.1109\/CVPR52729.2023.01213"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Azinovi\u0107, D., Martin-Brualla, R., Goldman, D.B., Nie\u00dfner, M., Thies, J.: Neural RGB-D surface reconstruction. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6290\u20136301 (2022)","DOI":"10.1109\/CVPR52688.2022.00619"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-NeRF: a multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5855\u20135864 (2021)","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"10_CR7","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":"10_CR8","unstructured":"Betker, J., et al.: Improving image generation with better captions. Comput. Sci. 2(3), 8 (2023). https:\/\/cdnopenai.com\/papers\/dall-e-3.pdf"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Chan, E.R., et al.: Efficient geometry-aware 3D generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16123\u201316133 (2022)","DOI":"10.1109\/CVPR52688.2022.01565"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Chan, E.R., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G.: $$\\pi $$-GAN: periodic implicit generative adversarial networks for 3D-aware image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5799\u20135809 (2021)","DOI":"10.1109\/CVPR46437.2021.00574"},{"key":"10_CR11","unstructured":"Chang, A.X., et al.: Shapenet: an information-rich 3D model repository. arXiv preprint arXiv:1512.03012 (2015)"},{"key":"10_CR12","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/978-3-031-19824-3_20","volume-title":"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":"10_CR13","unstructured":"Chen, A., Xu, Z., Wei, X., Tang, S., Su, H., Geiger, A.: Factor fields: a unified framework for neural fields and beyond. arXiv preprint arXiv:2302.01226 (2023)"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Chen, D.Z., Siddiqui, Y., Lee, H.Y., Tulyakov, S., Nie\u00dfner, M.: Text2tex: text-driven texture synthesis via diffusion models. arXiv preprint arXiv:2303.11396 (2023)","DOI":"10.1109\/ICCV51070.2023.01701"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Chen, H., et al.: Single-stage diffusion nerf: a unified approach to 3D generation and reconstruction. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 2416\u20132425 (2023)","DOI":"10.1109\/ICCV51070.2023.00229"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Chen, Z., Tagliasacchi, A., Zhang, H.: BSP-net: generating compact meshes via binary space partitioning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 45\u201354 (2020)","DOI":"10.1109\/CVPR42600.2020.00012"},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Cheng, Y.C., Lee, H.Y., Tulyakov, S., Schwing, A.G., Gui, L.Y.: Sdfusion: multimodal 3D shape completion, reconstruction, and generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4456\u20134465 (2023)","DOI":"10.1109\/CVPR52729.2023.00433"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Chou, G., Bahat, Y., Heide, F.: Diffusion-SDF: conditional generative modeling of signed distance functions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2262\u20132272 (2023)","DOI":"10.1109\/ICCV51070.2023.00215"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Collins, J., et al.: ABO: dataset and benchmarks for real-world 3D object understanding. In: CVPR (2022)","DOI":"10.1109\/CVPR52688.2022.02045"},{"issue":"9","key":"10_CR20","doi-asserted-by":"publisher","first-page":"10850","DOI":"10.1109\/TPAMI.2023.3261988","volume":"45","author":"FA Croitoru","year":"2023","unstructured":"Croitoru, F.A., Hondru, V., Ionescu, R.T., Shah, M.: Diffusion models in vision: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 45(9), 10850\u201310869 (2023)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10_CR21","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":"10_CR22","doi-asserted-by":"crossref","unstructured":"Deitke, M., et al.: Objaverse-xl: a universe of 10m+ 3D objects. In: Advances in Neural Information Processing Systems, vol. 36 (2024)","DOI":"10.1109\/CVPR52729.2023.01263"},{"key":"10_CR23","doi-asserted-by":"crossref","unstructured":"Deitke, M., et al.: Objaverse: a universe of annotated 3D objects. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13142\u201313153 (2023)","DOI":"10.1109\/CVPR52729.2023.01263"},{"key":"10_CR24","doi-asserted-by":"crossref","unstructured":"Deng, K., Liu, A., Zhu, J.Y., Ramanan, D.: Depth-supervised NeRF: fewer views and faster training for free. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12882\u201312891 (2022)","DOI":"10.1109\/CVPR52688.2022.01254"},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3354\u20133361. IEEE (2012)","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Heylen, J., et al.: Monocinis: camera independent monocular 3D object detection using instance segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 923\u2013934 (2021)","DOI":"10.1109\/ICCVW54120.2021.00108"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"H\u00f6llein, L., et al.: Viewdiff: 3D-consistent image generation with text-to-image models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2024)","DOI":"10.1109\/CVPR52733.2024.00482"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Jang, W., Agapito, L.: CodeNeRF: disentangled neural radiance fields for object categories. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 12949\u201312958 (2021)","DOI":"10.1109\/ICCV48922.2021.01271"},{"key":"10_CR29","doi-asserted-by":"crossref","unstructured":"Karnewar, A., Vedaldi, A., Novotny, D., Mitra, N.J.: Holodiffusion: training a 3D diffusion model using 2D images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 18423\u201318433 (2023)","DOI":"10.1109\/CVPR52729.2023.01767"},{"key":"10_CR30","doi-asserted-by":"crossref","unstructured":"Kato, H., Ushiku, Y., Harada, T.: Neural 3D mesh renderer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3907\u20133916 (2018)","DOI":"10.1109\/CVPR.2018.00411"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Lin, K.E., Lin, Y.C., Lai, W.S., Lin, T.Y., Shih, Y.C., Ramamoorthi, R.: Vision transformer for NeRF-based view synthesis from a single input image. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 806\u2013815 (2023)","DOI":"10.1109\/WACV56688.2023.00087"},{"key":"10_CR32","doi-asserted-by":"crossref","unstructured":"Liu, M., et al.: One-2-3-45++: fast single image to 3D objects with consistent multi-view generation and 3D diffusion. arXiv preprint arXiv:2311.07885 (2023)","DOI":"10.1109\/CVPR52733.2024.00960"},{"key":"10_CR33","doi-asserted-by":"crossref","unstructured":"Liu, R., Wu, R., Van\u00a0Hoorick, B., Tokmakov, P., Zakharov, S., Vondrick, C.: Zero-1-to-3: zero-shot one image to 3D object. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 9298\u20139309 (2023)","DOI":"10.1109\/ICCV51070.2023.00853"},{"key":"10_CR34","doi-asserted-by":"crossref","unstructured":"Liu, S., Li, T., Chen, W., Li, H.: Soft rasterizer: a differentiable renderer for image-based 3D reasoning. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7708\u20137717 (2019)","DOI":"10.1109\/ICCV.2019.00780"},{"key":"10_CR35","doi-asserted-by":"crossref","unstructured":"Luo, S., Hu, W.: Diffusion probabilistic models for 3D point cloud generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2837\u20132845 (2021)","DOI":"10.1109\/CVPR46437.2021.00286"},{"key":"10_CR36","doi-asserted-by":"crossref","unstructured":"Metzer, G., Richardson, E., Patashnik, O., Giryes, R., Cohen-Or, D.: Latent-NeRF for shape-guided generation of 3D shapes and textures. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12663\u201312673 (2023)","DOI":"10.1109\/CVPR52729.2023.01218"},{"key":"10_CR37","doi-asserted-by":"crossref","unstructured":"Mildenhall, B., Hedman, P., Martin-Brualla, R., Srinivasan, P.P., Barron, J.T.: NeRF in the dark: high dynamic range view synthesis from noisy raw images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16190\u201316199 (2022)","DOI":"10.1109\/CVPR52688.2022.01571"},{"issue":"1","key":"10_CR38","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":"10_CR39","doi-asserted-by":"crossref","unstructured":"M\u00fcller, N., Siddiqui, Y., Porzi, L., Bulo, S.R., Kontschieder, P., Nie\u00dfner, M.: Diffrf: rendering-guided 3D radiance field diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4328\u20134338 (2023)","DOI":"10.1109\/CVPR52729.2023.00421"},{"issue":"4","key":"10_CR40","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":"10_CR41","unstructured":"Nam, G., Khlifi, M., Rodriguez, A., Tono, A., Zhou, L., Guerrero, P.: 3D-LDM: neural implicit 3D shape generation with latent diffusion models. arXiv preprint arXiv:2212.00842 (2022)"},{"key":"10_CR42","unstructured":"Nash, C., Ganin, Y., Eslami, S.A., Battaglia, P.: Polygen: an autoregressive generative model of 3D meshes. In: International Conference on Machine Learning, pp. 7220\u20137229. PMLR (2020)"},{"key":"10_CR43","unstructured":"Nichol, A., et al.: Glide: towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021)"},{"key":"10_CR44","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Barron, J.T., Mildenhall, B., Sajjadi, M.S., Geiger, A., Radwan, N.: RegNeRF: regularizing neural radiance fields for view synthesis from sparse inputs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5480\u20135490 (2022)","DOI":"10.1109\/CVPR52688.2022.00540"},{"key":"10_CR45","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Geiger, A.: Giraffe: representing scenes as compositional generative neural feature fields. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11453\u201311464 (2021)","DOI":"10.1109\/CVPR46437.2021.01129"},{"key":"10_CR46","doi-asserted-by":"crossref","unstructured":"Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.: Differentiable volumetric rendering: learning implicit 3D representations without 3D supervision. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3504\u20133515 (2020)","DOI":"10.1109\/CVPR42600.2020.00356"},{"key":"10_CR47","doi-asserted-by":"crossref","unstructured":"Park, J.J., Florence, P., Straub, J., Newcombe, R., Lovegrove, S.: Deepsdf: learning continuous signed distance functions for shape representation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 165\u2013174 (2019)","DOI":"10.1109\/CVPR.2019.00025"},{"key":"10_CR48","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":"10_CR49","doi-asserted-by":"crossref","unstructured":"Roessle, B., Barron, J.T., Mildenhall, B., Srinivasan, P.P., Nie\u00dfner, M.: Dense depth priors for neural radiance fields from sparse input views. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12892\u201312901 (2022)","DOI":"10.1109\/CVPR52688.2022.01255"},{"key":"10_CR50","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"10_CR51","unstructured":"Schwarz, K., Liao, Y., Niemeyer, M., Geiger, A.: Graf: generative radiance fields for 3D-aware image synthesis. In: Advances in Neural Information Processing Systems, vol. 33, pp. 20154\u201320166 (2020)"},{"key":"10_CR52","doi-asserted-by":"crossref","unstructured":"Shue, J.R., Chan, E.R., Po, R., Ankner, Z., Wu, J., Wetzstein, G.: 3D neural field generation using triplane diffusion. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 20875\u201320886 (2023)","DOI":"10.1109\/CVPR52729.2023.02000"},{"key":"10_CR53","unstructured":"Sitzmann, V., Zollh\u00f6fer, M., Wetzstein, G.: Scene representation networks: Continuous 3d-structure-aware neural scene representations. In: Advances in Neural Information Processing Systems, vol. 32 (2019)"},{"key":"10_CR54","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"10_CR55","doi-asserted-by":"crossref","unstructured":"Sun, J., Xie, Y., Chen, L., Zhou, X., Bao, H.: Neuralrecon: real-time coherent 3D reconstruction from monocular video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15598\u201315607 (2021)","DOI":"10.1109\/CVPR46437.2021.01534"},{"key":"10_CR56","doi-asserted-by":"crossref","unstructured":"Szymanowicz, S., Rupprecht, C., Vedaldi, A.: Viewset diffusion:(0-) image-conditioned 3D generative models from 2D data. arXiv preprint arXiv:2306.07881 (2023)","DOI":"10.1109\/ICCV51070.2023.00814"},{"key":"10_CR57","unstructured":"Tagliasacchi, A., Mildenhall, B.: Volume rendering digest (for NeRF). arXiv preprint arXiv:2209.02417 (2022)"},{"key":"10_CR58","doi-asserted-by":"crossref","unstructured":"Tang, J., et al.: Delicate textured mesh recovery from NeRF via adaptive surface refinement. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 17739\u201317749 (2023)","DOI":"10.1109\/ICCV51070.2023.01626"},{"key":"10_CR59","doi-asserted-by":"crossref","unstructured":"Tang, J., et al.: Make-It-3D: high-fidelity 3D creation from a single image with diffusion prior. arXiv preprint arXiv:2303.14184 (2023)","DOI":"10.1109\/ICCV51070.2023.02086"},{"key":"10_CR60","doi-asserted-by":"crossref","unstructured":"Verbin, D., Hedman, P., Mildenhall, B., Zickler, T., Barron, J.T., Srinivasan, P.P.: Ref-NeRF: structured view-dependent appearance for neural radiance fields. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5481\u20135490. IEEE (2022)","DOI":"10.1109\/CVPR52688.2022.00541"},{"key":"10_CR61","doi-asserted-by":"crossref","unstructured":"Wang, H., Du, X., Li, J., Yeh, R.A., Shakhnarovich, G.: Score jacobian chaining: lifting pretrained 2D diffusion models for 3D generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12619\u201312629 (2023)","DOI":"10.1109\/CVPR52729.2023.01214"},{"key":"10_CR62","unstructured":"Wang, P., Liu, L., Liu, Y., Theobalt, C., Komura, T., Wang, W.: Neus: learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689 (2021)"},{"key":"10_CR63","doi-asserted-by":"crossref","unstructured":"Wei, Y., Liu, S., Rao, Y., Zhao, W., Lu, J., Zhou, J.: NerfingMVS: guided optimization of neural radiance fields for indoor multi-view stereo. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 5610\u20135619 (2021)","DOI":"10.1109\/ICCV48922.2021.00556"},{"key":"10_CR64","unstructured":"Wu, J., Zhang, C., Xue, T., Freeman, B., Tenenbaum, J.: Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In: Advances in Neural Information Processing Systems, vol. 29 (2016)"},{"key":"10_CR65","doi-asserted-by":"crossref","unstructured":"Yariv, L., et al.: Bakedsdf: meshing neural SDFs for real-time view synthesis. arXiv preprint arXiv:2302.14859 (2023)","DOI":"10.1145\/3588432.3591536"},{"key":"10_CR66","doi-asserted-by":"crossref","unstructured":"Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3836\u20133847 (2023)","DOI":"10.1109\/ICCV51070.2023.00355"}],"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\/978-3-031-72673-6_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T16:06:54Z","timestamp":1729526814000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-72673-6_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,22]]},"ISBN":["9783031726729","9783031726736"],"references-count":66,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-72673-6_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,22]]},"assertion":[{"value":"22 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"}}]}}