{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T03:46:53Z","timestamp":1784173613710,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T00:00:00Z","timestamp":1669680000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61733007, No. 61876212"],"award-info":[{"award-number":["No. 61733007, No. 61876212"]}],"id":[{"id":"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":[[2022,11,29]]},"DOI":"10.1145\/3550469.3555383","type":"proceedings-article","created":{"date-parts":[[2022,11,30]],"date-time":"2022-11-30T11:07:54Z","timestamp":1669806474000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":275,"title":["Fast Dynamic Radiance Fields with Time-Aware Neural Voxels"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0322-4582","authenticated-orcid":false,"given":"Jiemin","family":"Fang","sequence":"first","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7316-7547","authenticated-orcid":false,"given":"Taoran","family":"Yi","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6732-7823","authenticated-orcid":false,"given":"Xinggang","family":"Wang","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4831-9451","authenticated-orcid":false,"given":"Lingxi","family":"Xie","sequence":"additional","affiliation":[{"name":"Huawei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6337-5748","authenticated-orcid":false,"given":"Xiaopeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Huawei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4582-7488","authenticated-orcid":false,"given":"Wenyu","family":"Liu","sequence":"additional","affiliation":[{"name":"Huazhong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6093-5199","authenticated-orcid":false,"given":"Matthias","family":"Nie\u00dfner","sequence":"additional","affiliation":[{"name":"Technical University of Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7252-5047","authenticated-orcid":false,"given":"Qi","family":"Tian","sequence":"additional","affiliation":[{"name":"Huawei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,11,30]]},"reference":[{"key":"e_1_3_2_3_1_1","volume-title":"NeRF in detail: Learning to sample for view synthesis. arXiv:2106.05264","author":"Arandjelovi\u0107 Relja","year":"2021","unstructured":"Relja Arandjelovi\u0107 and Andrew Zisserman. 2021. NeRF in detail: Learning to sample for view synthesis. arXiv:2106.05264 (2021)."},{"key":"e_1_3_2_3_2_1","volume-title":"T\u00f6rf: Time-of-flight radiance fields for dynamic scene view synthesis. NeurIPS","author":"Attal Benjamin","year":"2021","unstructured":"Benjamin Attal, Eliot Laidlaw, Aaron Gokaslan, Changil Kim, Christian Richardt, James Tompkin, and Matthew O\u2019Toole. 2021. T\u00f6rf: Time-of-flight radiance fields for dynamic scene view synthesis. NeurIPS (2021)."},{"key":"e_1_3_2_3_3_1","doi-asserted-by":"crossref","unstructured":"Jonathan\u00a0T. Barron Ben Mildenhall Matthew Tancik Peter Hedman Ricardo Martin-Brualla and Pratul\u00a0P. Srinivasan. 2021. Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00580"},{"key":"e_1_3_2_3_4_1","volume-title":"TensoRF: Tensorial Radiance Fields. arXiv:2203.09517","author":"Chen Anpei","year":"2022","unstructured":"Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. 2022. TensoRF: Tensorial Radiance Fields. arXiv:2203.09517 (2022)."},{"key":"e_1_3_2_3_5_1","doi-asserted-by":"crossref","unstructured":"Anpei Chen Zexiang Xu Fuqiang Zhao Xiaoshuai Zhang Fanbo Xiang Jingyi Yu and Hao Su. 2021. MVSNeRF: Fast Generalizable Radiance Field Reconstruction From Multi-View Stereo. In ICCV.","DOI":"10.1109\/ICCV48922.2021.01386"},{"key":"e_1_3_2_3_6_1","volume-title":"Depth-supervised nerf: Fewer views and faster training for free. arXiv:2107.02791","author":"Deng Kangle","year":"2021","unstructured":"Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan. 2021. Depth-supervised nerf: Fewer views and faster training for free. arXiv:2107.02791 (2021)."},{"key":"e_1_3_2_3_7_1","unstructured":"Yilun Du Yinan Zhang Hong-Xing Yu Joshua\u00a0B Tenenbaum and Jiajun Wu. 2021. Neural radiance flow for 4d view synthesis and video processing. In ICCV."},{"key":"e_1_3_2_3_8_1","volume-title":"NeuSample: Neural Sample Field for Efficient View Synthesis. arXiv:2111.15552","author":"Fang Jiemin","year":"2021","unstructured":"Jiemin Fang, Lingxi Xie, Xinggang Wang, Xiaopeng Zhang, Wenyu Liu, and Qi Tian. 2021. NeuSample: Neural Sample Field for Efficient View Synthesis. arXiv:2111.15552 (2021)."},{"key":"e_1_3_2_3_9_1","doi-asserted-by":"crossref","unstructured":"Chen Gao Ayush Saraf Johannes Kopf and Jia-Bin Huang. 2021. Dynamic view synthesis from dynamic monocular video. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00566"},{"key":"e_1_3_2_3_10_1","doi-asserted-by":"crossref","unstructured":"Stephan\u00a0J. Garbin Marek Kowalski Matthew Johnson Jamie Shotton and Julien Valentin. 2021. FastNeRF: High-Fidelity Neural Rendering at 200FPS. In ICCV.","DOI":"10.1109\/ICCV48922.2021.01408"},{"key":"e_1_3_2_3_11_1","doi-asserted-by":"crossref","unstructured":"Peter Hedman Pratul\u00a0P. Srinivasan Ben Mildenhall Jonathan\u00a0T. Barron and Paul Debevec. 2021. Baking Neural Radiance Fields for Real-Time View Synthesis. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00582"},{"key":"e_1_3_2_3_12_1","doi-asserted-by":"crossref","unstructured":"James\u00a0T Kajiya and Brian\u00a0P Von\u00a0Herzen. 1984. Ray tracing volume densities. ACM SIGGRAPH computer graphics(1984).","DOI":"10.1145\/800031.808594"},{"key":"e_1_3_2_3_13_1","volume-title":"Adam: A Method for Stochastic Optimization. In ICLR.","author":"Kingma P","year":"2015","unstructured":"Diederik\u00a0P Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR."},{"key":"e_1_3_2_3_14_1","series-title":"SIAM review","volume-title":"Tensor decompositions and applications","author":"Kolda G","year":"2009","unstructured":"Tamara\u00a0G Kolda and Brett\u00a0W Bader. 2009. Tensor decompositions and applications. SIAM review (2009)."},{"key":"e_1_3_2_3_15_1","volume-title":"Neural 3d video synthesis. arXiv:2103.02597","author":"Li Tianye","year":"2021","unstructured":"Tianye Li, Mira Slavcheva, Michael Zollhoefer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Steven Lovegrove, Michael Goesele, and Zhaoyang Lv. 2021b. Neural 3d video synthesis. arXiv:2103.02597 (2021)."},{"key":"e_1_3_2_3_16_1","unstructured":"Zhengqi Li Simon Niklaus Noah Snavely and Oliver Wang. 2021a. Neural scene flow fields for space-time view synthesis of dynamic scenes. In CVPR."},{"key":"e_1_3_2_3_17_1","volume-title":"Barf: Bundle-adjusting neural radiance fields. In ICCV.","author":"Lin Chen-Hsuan","year":"2021","unstructured":"Chen-Hsuan Lin, Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. 2021. Barf: Bundle-adjusting neural radiance fields. In ICCV."},{"key":"e_1_3_2_3_18_1","volume-title":"Autoint: Automatic integration for fast neural","author":"Lindell B","year":"2021","unstructured":"David\u00a0B Lindell, Julien\u00a0NP Martel, and Gordon Wetzstein. 2021. Autoint: Automatic integration for fast neural volume rendering. In CVPR."},{"key":"e_1_3_2_3_19_1","unstructured":"Lingjie Liu Jiatao Gu Kyaw\u00a0Zaw Lin Tat-Seng Chua and Christian Theobalt. 2020. Neural Sparse Voxel Fields. In NeurIPS."},{"key":"e_1_3_2_3_20_1","doi-asserted-by":"crossref","unstructured":"Yuan Liu Sida Peng Lingjie Liu Qianqian Wang Peng Wang Theobalt Christian Xiaowei Zhou and Wenping Wang. 2022. Neural Rays for Occlusion-aware Image-based Rendering. In CVPR.","DOI":"10.1109\/CVPR52688.2022.00767"},{"key":"e_1_3_2_3_21_1","doi-asserted-by":"crossref","unstructured":"Stephen Lombardi Tomas Simon Jason Saragih Gabriel Schwartz Andreas Lehrmann and Yaser Sheikh. 2019. Neural volumes: learning dynamic renderable volumes from images. ACM Transactions on Graphics(2019).","DOI":"10.1145\/3306346.3323020"},{"key":"e_1_3_2_3_22_1","doi-asserted-by":"crossref","unstructured":"Ricardo Martin-Brualla Noha Radwan Mehdi S.\u00a0M. Sajjadi Jonathan\u00a0T. Barron Alexey Dosovitskiy and Daniel Duckworth. 2021. NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00713"},{"key":"e_1_3_2_3_23_1","volume-title":"Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV.","author":"Mildenhall Ben","year":"2020","unstructured":"Ben Mildenhall, Pratul\u00a0P Srinivasan, Matthew Tancik, Jonathan\u00a0T Barron, Ravi Ramamoorthi, and Ren Ng. 2020. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV."},{"key":"e_1_3_2_3_24_1","volume-title":"Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. arXiv:2201.05989","author":"M\u00fcller Thomas","year":"2022","unstructured":"Thomas M\u00fcller, Alex Evans, Christoph Schied, and Alexander Keller. 2022. Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. arXiv:2201.05989 (2022)."},{"key":"e_1_3_2_3_25_1","doi-asserted-by":"crossref","unstructured":"Thomas Neff Pascal Stadlbauer Mathias Parger Andreas Kurz Joerg\u00a0H. Mueller Chakravarty R.\u00a0Alla Chaitanya Anton\u00a0S. Kaplanyan and Markus Steinberger. 2021. DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks. Computer Graphics Forum(2021).","DOI":"10.1111\/cgf.14340"},{"key":"e_1_3_2_3_26_1","doi-asserted-by":"crossref","unstructured":"Matthias Nie\u00dfner Michael Zollh\u00f6fer Shahram Izadi and Marc Stamminger. 2013. Real-time 3D reconstruction at scale using voxel hashing. ACM Transactions on Graphics (ToG)(2013).","DOI":"10.1145\/2508363.2508374"},{"key":"e_1_3_2_3_27_1","doi-asserted-by":"crossref","unstructured":"Atsuhiro Noguchi Xiao Sun Stephen Lin and Tatsuya Harada. 2021. Neural articulated radiance field. In ICCV.","DOI":"10.1109\/ICCV48922.2021.00571"},{"key":"e_1_3_2_3_28_1","volume-title":"Nerfies: Deformable Neural Radiance Fields. ICCV","author":"Park Keunhong","year":"2021","unstructured":"Keunhong Park, Utkarsh Sinha, Jonathan\u00a0T. Barron, Sofien Bouaziz, Dan\u00a0B Goldman, Steven\u00a0M. Seitz, and Ricardo Martin-Brualla. 2021a. Nerfies: Deformable Neural Radiance Fields. ICCV (2021)."},{"key":"e_1_3_2_3_29_1","doi-asserted-by":"crossref","unstructured":"Keunhong Park Utkarsh Sinha Peter Hedman Jonathan\u00a0T. Barron Sofien Bouaziz Dan\u00a0B Goldman Ricardo Martin-Brualla and Steven\u00a0M. Seitz. 2021b. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. ACM Trans. Graph. (2021).","DOI":"10.1145\/3478513.3480487"},{"key":"e_1_3_2_3_30_1","volume-title":"Pytorch: An imperative style, high-performance deep learning library. NeurIPS","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, 2019. Pytorch: An imperative style, high-performance deep learning library. NeurIPS (2019)."},{"key":"e_1_3_2_3_31_1","doi-asserted-by":"crossref","unstructured":"Martin Piala and Ronald Clark. 2021. TermiNeRF: Ray Termination Prediction for Efficient Neural Rendering. In 3DV.","DOI":"10.1109\/3DV53792.2021.00118"},{"key":"e_1_3_2_3_32_1","volume-title":"D-nerf: Neural radiance fields for dynamic scenes. In CVPR.","author":"Pumarola Albert","year":"2021","unstructured":"Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. 2021. D-nerf: Neural radiance fields for dynamic scenes. In CVPR."},{"key":"e_1_3_2_3_33_1","doi-asserted-by":"crossref","unstructured":"Christian Reiser Songyou Peng Yiyi Liao and Andreas Geiger. 2021. KiloNeRF: Speeding Up Neural Radiance Fields With Thousands of Tiny MLPs. In ICCV.","DOI":"10.1109\/ICCV48922.2021.01407"},{"key":"e_1_3_2_3_34_1","volume-title":"Grad-cam: Visual explanations from deep networks via gradient-based localization. In ICCV.","author":"Selvaraju R","year":"2017","unstructured":"Ramprasaath\u00a0R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra. 2017. Grad-cam: Visual explanations from deep networks via gradient-based localization. In ICCV."},{"key":"e_1_3_2_3_35_1","unstructured":"Vincent Sitzmann Semon Rezchikov William\u00a0T. Freeman Joshua\u00a0B. Tenenbaum and Fredo Durand. 2021. Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering. In NeurIPS."},{"key":"e_1_3_2_3_36_1","volume-title":"Deepvoxels: Learning persistent 3d feature embeddings. In CVPR.","author":"Sitzmann Vincent","year":"2019","unstructured":"Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nie\u00dfner, Gordon Wetzstein, and Michael Zollhofer. 2019. Deepvoxels: Learning persistent 3d feature embeddings. In CVPR."},{"key":"e_1_3_2_3_37_1","volume-title":"A-nerf: Articulated neural radiance fields for learning human shape, appearance, and pose. NeurIPS","author":"Su Shih-Yang","year":"2021","unstructured":"Shih-Yang Su, Frank Yu, Michael Zollh\u00f6fer, and Helge Rhodin. 2021. A-nerf: Articulated neural radiance fields for learning human shape, appearance, and pose. NeurIPS (2021)."},{"key":"e_1_3_2_3_38_1","doi-asserted-by":"crossref","unstructured":"Cheng Sun Min Sun and Hwann-Tzong Chen. 2022. Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction. In CVPR.","DOI":"10.1109\/CVPR52688.2022.00538"},{"key":"e_1_3_2_3_39_1","doi-asserted-by":"crossref","unstructured":"Towaki Takikawa Alex Evans Jonathan Tremblay Thomas M\u00fcller Morgan McGuire Alec Jacobson and Sanja Fidler. 2022. Variable Bitrate Neural Fields. In SIGGRAPH.","DOI":"10.1145\/3528233.3530727"},{"key":"e_1_3_2_3_40_1","doi-asserted-by":"crossref","unstructured":"Edgar Tretschk Ayush Tewari Vladislav Golyanik Michael Zollh\u00f6fer Christoph Lassner and Christian Theobalt. 2021a. Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video. In ICCV.","DOI":"10.1109\/ICCV48922.2021.01272"},{"key":"e_1_3_2_3_41_1","doi-asserted-by":"crossref","unstructured":"Edgar Tretschk Ayush Tewari Vladislav Golyanik Michael Zollh\u00f6fer Christoph Lassner and Christian Theobalt. 2021b. Non-rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video. In ICCV.","DOI":"10.1109\/ICCV48922.2021.01272"},{"key":"e_1_3_2_3_42_1","volume-title":"Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields. CVPR","author":"Verbin Dor","year":"2022","unstructured":"Dor Verbin, Peter Hedman, Ben Mildenhall, Todd Zickler, Jonathan\u00a0T. Barron, and Pratul\u00a0P. Srinivasan. 2022. Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields. CVPR (2022)."},{"key":"e_1_3_2_3_43_1","volume-title":"Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time. arXiv:2202.08614","author":"Wang Liao","year":"2022","unstructured":"Liao Wang, Jiakai Zhang, Xinhang Liu, Fuqiang Zhao, Yanshun Zhang, Yingliang Zhang, Minye Wu, Lan Xu, and Jingyi Yu. 2022. Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time. arXiv:2202.08614 (2022)."},{"key":"e_1_3_2_3_44_1","volume-title":"Ibrnet: Learning multi-view image-based rendering. In CVPR.","author":"Wang Qianqian","year":"2021","unstructured":"Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul\u00a0P Srinivasan, Howard Zhou, Jonathan\u00a0T Barron, Ricardo Martin-Brualla, Noah Snavely, and Thomas Funkhouser. 2021a. Ibrnet: Learning multi-view image-based rendering. In CVPR."},{"key":"e_1_3_2_3_45_1","volume-title":"Image quality assessment: from error visibility to structural similarity","author":"Wang Zhou","year":"2004","unstructured":"Zhou Wang, Alan\u00a0C Bovik, Hamid\u00a0R Sheikh, and Eero\u00a0P Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE TIP (2004)."},{"key":"e_1_3_2_3_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACSSC.2003.1292216"},{"key":"e_1_3_2_3_47_1","volume-title":"NeRF\u2013: Neural radiance fields without known camera parameters. arXiv:2102.07064","author":"Wang Zirui","year":"2021","unstructured":"Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor\u00a0Adrian Prisacariu. 2021b. NeRF\u2013: Neural radiance fields without known camera parameters. arXiv:2102.07064 (2021)."},{"key":"e_1_3_2_3_48_1","volume-title":"Humannerf: Free-viewpoint rendering of moving people from monocular video. In CVPR.","author":"Weng Chung-Yi","year":"2022","unstructured":"Chung-Yi Weng, Brian Curless, Pratul\u00a0P Srinivasan, Jonathan\u00a0T Barron, and Ira Kemelmacher-Shlizerman. 2022. Humannerf: Free-viewpoint rendering of moving people from monocular video. In CVPR."},{"key":"e_1_3_2_3_49_1","volume-title":"Nex: Real-time view synthesis with neural basis expansion. In CVPR.","author":"Wizadwongsa Suttisak","year":"2021","unstructured":"Suttisak Wizadwongsa, Pakkapon Phongthawee, Jiraphon Yenphraphai, and Supasorn Suwajanakorn. 2021. Nex: Real-time view synthesis with neural basis expansion. In CVPR."},{"key":"e_1_3_2_3_50_1","doi-asserted-by":"crossref","unstructured":"Wenqi Xian Jia-Bin Huang Johannes Kopf and Changil Kim. 2021. Space-time Neural Irradiance Fields for Free-Viewpoint Video. In CVPR.","DOI":"10.1109\/CVPR46437.2021.00930"},{"key":"e_1_3_2_3_51_1","volume-title":"H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion. NeurIPS","author":"Xu Hongyi","year":"2021","unstructured":"Hongyi Xu, Thiemo Alldieck, and Cristian Sminchisescu. 2021. H-nerf: Neural radiance fields for rendering and temporal reconstruction of humans in motion. NeurIPS (2021)."},{"key":"e_1_3_2_3_52_1","volume-title":"Plenoxels: Radiance Fields without Neural Networks. In CVPR.","author":"Yu Alex","year":"2022","unstructured":"Alex Yu, Sara Fridovich-Keil, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa. 2022. Plenoxels: Radiance Fields without Neural Networks. In CVPR."},{"key":"e_1_3_2_3_53_1","unstructured":"Alex Yu Ruilong Li Matthew Tancik Hao Li Ren Ng and Angjoo Kanazawa. 2021a. PlenOctrees for Real-Time Rendering of Neural Radiance Fields. In ICCV."},{"key":"e_1_3_2_3_54_1","unstructured":"Alex Yu Vickie Ye Matthew Tancik and Angjoo Kanazawa. 2021b. pixelNeRF: Neural radiance fields from one or few images. In CVPR."},{"key":"e_1_3_2_3_55_1","volume-title":"Analyzing and Improving Neural Radiance Fields. arXiv:2010.07492","author":"Zhang Kai","year":"2020","unstructured":"Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. 2020. NeRF++: Analyzing and Improving Neural Radiance Fields. arXiv:2010.07492 (2020)."},{"key":"e_1_3_2_3_56_1","doi-asserted-by":"crossref","unstructured":"Richard Zhang Phillip Isola Alexei\u00a0A Efros Eli Shechtman and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR.","DOI":"10.1109\/CVPR.2018.00068"}],"event":{"name":"SA '22: SIGGRAPH Asia 2022","location":"Daegu Republic of Korea","acronym":"SA '22","sponsor":["SIGGRAPH ACM Special Interest Group on Computer Graphics and Interactive Techniques"]},"container-title":["SIGGRAPH Asia 2022 Conference Papers"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3550469.3555383","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3550469.3555383","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:44Z","timestamp":1750182704000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3550469.3555383"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,29]]},"references-count":56,"alternative-id":["10.1145\/3550469.3555383","10.1145\/3550469"],"URL":"https:\/\/doi.org\/10.1145\/3550469.3555383","relation":{},"subject":[],"published":{"date-parts":[[2022,11,29]]},"assertion":[{"value":"2022-11-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}