research-article

NeRF-SR: High Quality Neural Radiance Fields using Supersampling

Authors:

Song-Hai Zhang,

Shi-Min HuAuthors Info & Claims

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

Pages 6445 - 6454

https://doi.org/10.1145/3503161.3547808

Published: 10 October 2022 Publication History

Abstract

We present NeRF-SR, a solution for high-resolution (HR) novel view synthesis with mostly low-resolution (LR) inputs. Our method is built upon Neural Radiance Fields (NeRF) that predicts per-point density and color with a multi-layer perceptron. While producing images at arbitrary scales, NeRF struggles with resolutions that go beyond observed images. Our key insight is that NeRF benefits from 3D consistency, which means an observed pixel absorbs information from nearby views. We first exploit it by a super-sampling strategy that shoots multiple rays at each image pixel, which further enforces multi-view constraint at a sub-pixel level. Then, we show that NeRF-SR can further boost the performance of super-sampling by a refinement network that leverages the estimated depth at hand to hallucinate details from related patches on only one HR reference image. Experiment results demonstrate that NeRF-SR generates high-quality results for novel view synthesis at HR on both synthetic and real-world datasets without any external information. Project page: https://cwchenwang.github.io/NeRF-SR

Supplementary Material

MP4 File (MM22-fp0288.mp4)

Presentation video for NeRF-SR, in which we propose a framework for synthesizing HR novel views with only LR images and can flexibly adapt to limited HR reference images.

Download
23.81 MB

References

[1]

Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. 2020. Neural point-based graphics. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXII 16. Springer, 696--712.

[2]

Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, and Pratul P. Srinivasan. 2021. Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields. In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10--17, 2021. IEEE, 5835--5844. https://doi.org/10.1109/ICCV48922.2021.00580

[3]

Yinbo Chen, Sifei Liu, and Xiaolong Wang. 2021. Learning continuous image representation with local implicit image function. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8628--8638.

[4]

Zhiqin Chen and Hao Zhang. 2019. Learning implicit fields for generative shape modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5939--5948.

[5]

Inchang Choi, Orazio Gallo, Alejandro Troccoli, Min H Kim, and Jan Kautz. 2019. Extreme view synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 7781--7790.

[6]

Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2014. Learning a deep convolutional network for image super-resolution. In European conference on computer vision. Springer, 184--199.

[7]

Chao Dong, Chen Change Loy, Kaiming He, and Xiaoou Tang. 2015. Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence 38, 2 (2015), 295--307.

[8]

Yueqi Duan, Haidong Zhu, He Wang, Li Yi, Ram Nevatia, and Leonidas J Guibas. 2020. Curriculum deepsdf. In European Conference on Computer Vision. Springer, 51--67.

Digital Library

[9]

John Flynn, Michael Broxton, Paul Debevec, MatthewDuVall, Graham Fyffe, Ryan Overbeck, Noah Snavely, and Richard Tucker. 2019. Deepview: View synthesis with learned gradient descent. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2367--2376.

[10]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems 27 (2014).

[11]

Steven J Gortler, Radek Grzeszczuk, Richard Szeliski, and Michael F Cohen. 1996. The lumigraph. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. 43--54.

Digital Library

[12]

Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow. 2018. Deep blending for free-viewpoint image-based rendering. ACM Transactions on Graphics (TOG) 37, 6 (2018), 1--15.

Digital Library

[13]

Philipp Henzler, Niloy J Mitra, and Tobias Ritschel. 2020. Learning a neural 3d texture space from 2d exemplars. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8356--8364.

[14]

Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros. 2017. Image-toimage translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1125--1134.

[15]

Wonbong Jang and Lourdes Agapito. 2021. CodeNeRF: Disentangled Neural Radiance Fields for Object Categories. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 12949--12958.

[16]

James T Kajiya and Brian P Von Herzen. 1984. Ray tracing volume densities. ACM SIGGRAPH computer graphics 18, 3 (1984), 165--174.

[17]

Nima Khademi Kalantari, Ting-Chun Wang, and Ravi Ramamoorthi. 2016. Learning-based view synthesis for light field cameras. ACM Transactions on Graphics (TOG) 35, 6 (2016), 1--10.

Digital Library

[18]

Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada. 2018. Neural 3d mesh renderer. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3907--3916.

[19]

Kwang In Kim and Younghee Kwon. 2010. Single-image super-resolution using sparse regression and natural image prior. IEEE transactions on pattern analysis and machine intelligence 32, 6 (2010), 1127--1133.

[20]

Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, et al. 2017. Photo-realistic single image super-resolution using a generative adversarial network. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4681--4690.

[21]

Marc Levoy and Pat Hanrahan. 1996. Light field rendering. In Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. 31--42.

Digital Library

[22]

Jiaxin Li, Zijian Feng, Qi She, Henghui Ding, Changhu Wang, and Gim Hee Lee. 2021. MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 12578--12588.

[23]

Zhengqi Li, Wenqi Xian, Abe Davis, and Noah Snavely. 2020. Crowdsampling the plenoptic function. In European Conference on Computer Vision. Springer, 178--196.

Digital Library

[24]

Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. 2021. Swinir: Image restoration using swin transformer. In Proceedings f the IEEE/CVF International Conference on Computer Vision. 1833--1844.

[25]

Chen-Hsuan Lin,Wei-Chiu Ma, Antonio Torralba, and Simon Lucey. 2021. BARF: Bundle-Adjusting Neural Radiance Fields. arXiv preprint arXiv:2104.06405 (2021).

[26]

Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. 2020. Neural sparse voxel fields. Advances in Neural Information Processing Systems 33 (2020), 15651--15663.

[27]

Shichen Liu, Tianye Li, Weikai Chen, and Hao Li. 2019. Soft rasterizer: A differentiable renderer for image-based 3d reasoning. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 7708--7717.

[28]

Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovitskiy, and Daniel Duckworth. 2021. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7210--7219.

[29]

Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, and Cynthia Rudin. 2020. Pulse: Self-supervised photo upsampling via latent space exploration of generative models. In Proceedings of the ieee/cvf conference on computer vision and pattern recognition. 2437--2445.

[30]

Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4460--4470.

[31]

Ben Mildenhall, Pratul P Srinivasan, Rodrigo Ortiz-Cayon, Nima Khademi Kalantari, Ravi Ramamoorthi, Ren Ng, and Abhishek Kar. 2019. Local light field fusion: Practical view synthesis with prescriptive sampling guidelines. ACM Transactions on Graphics (TOG) 38, 4 (2019), 1--14.

Digital Library

[32]

Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. 2020. Nerf: Representing scenes as neural radiance fields for view synthesis. In European conference on computer vision. Springer, 405--421.

Digital Library

[33]

Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger. 2020. Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3504--3515.

[34]

Simon Niklaus, Long Mai, Jimei Yang, and Feng Liu. 2019. 3d ken burns effect from a single image. ACM Transactions on Graphics (TOG) 38, 6 (2019), 1--15.

Digital Library

[35]

Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. 2019. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 165--174.

[36]

Keunhong Park, Utkarsh Sinha, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Steven M Seitz, and Ricardo Martin-Brualla. 2021. Nerfies: Deformable neural radiance fields. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5865--5874.

[37]

Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-Brualla, and StevenMSeitz. 2021. HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields. arXiv preprint arXiv:2106.13228 (2021).

[38]

Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. 2020. Convolutional occupancy networks. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part III 16. Springer, 523--540.

[39]

Eric Penner and Li Zhang. 2017. Soft 3D reconstruction for view synthesis. ACM Transactions on Graphics (TOG) 36, 6 (2017), 1--11.

Digital Library

[40]

Albert Pumarola, Enric Corona, Gerard Pons-Moll, and Francesc Moreno-Noguer. 2021. D-nerf: Neural radiance fields for dynamic scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10318--10327.

[41]

Gernot Riegler and Vladlen Koltun. 2020. Free view synthesis. In European Conference on Computer Vision. Springer, 623--640.

Digital Library

[42]

Gernot Riegler and Vladlen Koltun. 2021. Stable view synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12216-- 12225.

[43]

Chris Rockwell, David F Fouhey, and Justin Johnson. 2021. Pixelsynth: Generating a 3d-consistent experience from a single image. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 14104--14113.

[44]

Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Angjoo Kanazawa, and Hao Li. 2019. Pifu: Pixel-aligned implicit function for highresolution clothed human digitization. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2304--2314.

[45]

Mehdi SM Sajjadi, Bernhard Scholkopf, and Michael Hirsch. 2017. Enhancenet: Single image super-resolution through automated texture synthesis. In Proceedings of the IEEE International Conference on Computer Vision. 4491--4500.

[46]

Johannes L Schonberger and Jan-Michael Frahm. 2016. Structure-from-motion revisited. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4104--4113.

[47]

Meng-Li Shih, Shih-Yang Su, Johannes Kopf, and Jia-Bin Huang. 2020. 3d photography using context-aware layered depth inpainting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8028--8038.

[48]

Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, GordonWetzstein, and Michael Zollhofer. 2019. Deepvoxels: Learning persistent 3d feature embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2437--2446.

[49]

Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein. 2019. Scene representation networks: Continuous 3d-structure-aware neural scene representations. arXiv preprint arXiv:1906.01618 (2019).

[50]

Pratul P Srinivasan, Richard Tucker, Jonathan T Barron, Ravi Ramamoorthi, Ren Ng, and Noah Snavely. 2019. Pushing the boundaries of view extrapolation with multiplane images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 175--184.

[51]

Jian Sun, Zongben Xu, and Heung-Yeung Shum. 2008. Image super-resolution using gradient profile prior. In 2008 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1--8.

[52]

Justus Thies, Michael Zollhöfer, and Matthias Nießner. 2019. Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG) 38, 4 (2019), 1--12.

Digital Library

[53]

Justus Thies, Michael Zollhöfer, Christian Theobalt, Marc Stamminger, and Matthias Nießner. 2020. Image-guided neural object rendering. In 8th International Conference on Learning Representations. OpenReview. net.

[54]

Alex Trevithick and Bo Yang. 2021. GRF: Learning a General Radiance Field for 3D Representation and Rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 15182--15192.

[55]

Richard Tucker and Noah Snavely. 2020. Single-view view synthesis with multiplane images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, 551--560.

[56]

Shubham Tulsiani, Richard Tucker, and Noah Snavely. 2018. Layer-structured 3d scene inference via view synthesis. In Proceedings of the European Conference on Computer Vision (ECCV). 302--317.

Digital Library

[57]

Zhaowen Wang, Ding Liu, Jianchao Yang, Wei Han, and Thomas Huang. 2015. Deep networks for image super-resolution with sparse prior. In Proceedings of the IEEE international conference on computer vision. 370--378.

Digital Library

[58]

ZhouWang, Eero P Simoncelli, and Alan C Bovik. 2003. Multiscale structural similarity for image quality assessment. In The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Vol. 2. Ieee, 1398--1402.

[59]

Zirui Wang, Shangzhe Wu, Weidi Xie, Min Chen, and Victor Adrian Prisacariu. 2021. NeRF--: Neural Radiance Fields Without Known Camera Parameters. arXiv preprint arXiv:2102.07064 (2021).

[60]

Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. 2020. Synsin: End-to-end view synthesis from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7467--7477.

[61]

Yanchun Xie, Jimin Xiao, Mingjie Sun, Chao Yao, and Kaizhu Huang. 2020. Feature representation matters: End-to-end learning for reference-based image superresolution. In European Conference on Computer Vision. Springer, 230--245.

Digital Library

[62]

Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo. 2020. Learning texture transformer network for image super-resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5791--5800.

[63]

Alex Yu, Vickie Ye, Matthew Tancik, and Angjoo Kanazawa. 2021. pixelnerf: Neural radiance fields from one or few images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4578--4587.

[64]

Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. 2020. Nerf: Analyzing and improving neural radiance fields. arXiv preprint arXiv:2010.07492 (2020).

[65]

Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE conference on computer vision and pattern recognition. 586--595.

[66]

Zhifei Zhang, Zhaowen Wang, Zhe Lin, and Hairong Qi. 2019. Image superresolution by neural texture transfer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7982--7991.

[67]

Haitian Zheng, Minghao Guo, Haoqian Wang, Yebin Liu, and Lu Fang. 2017. Combining exemplar-based approach and learning-based approach for light field super-resolution using a hybrid imaging system. In Proceedings of the IEEE International Conference on Computer Vision Workshops. 2481--2486.

[68]

Tinghui Zhou, Richard Tucker, John Flynn, Graham Fyffe, and Noah Snavely. 2018. Stereo magnification: learning view synthesis using multiplane images. ACM Transactions on Graphics (TOG) 37, 4 (2018), 1--12.

Digital Library

[69]

M Zontak and M Irani. 2011. Internal statistics of a single natural image. In Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. 977--984.

Digital Library

Cited By

Huang W(2025)Novel View Synthesis Based on Similar PerspectiveComputer Animation and Virtual Worlds10.1002/cav.7000636:1Online publication date: 7-Jan-2025
https://doi.org/10.1002/cav.70006
Jin PYu Z(2024)Research on 3D Visualization of Drone Scenes Based on Neural Radiance FieldsElectronics10.3390/electronics1309168213:9(1682)Online publication date: 26-Apr-2024
https://doi.org/10.3390/electronics13091682
Cai YLi RLiu L(2024)MV2MV: Multi-View Image Translation via View-Consistent Diffusion ModelsACM Transactions on Graphics10.1145/368797743:6(1-12)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687977
Show More Cited By

Index Terms

NeRF-SR: High Quality Neural Radiance Fields using Supersampling
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
  2. Computer graphics

Recommendations

DE-NeRF: DEcoupled Neural Radiance Fields for View-Consistent Appearance Editing and High-Frequency Environmental Relighting
SIGGRAPH '23: ACM SIGGRAPH 2023 Conference Proceedings

Neural Radiance Fields (NeRF) have shown promising results in novel view synthesis. While achieving state-of-the-art rendering results, NeRF usually encodes all properties related to geometry and appearance of the scene together into several MLP (Multi-...
Display supersampling

Supersampling is widely used by graphics hardware to render anti-aliased images. In conventional supersampling, multiple scene samples are computationally combined to produce a single screen pixel. We consider a novel imaging paradigm that we call ...
Hi-NeRF: Hybridizing 2D Inpainting with Neural Radiance Fields for 3D Scene Inpainting
Computer Vision – ACCV 2024
Abstract
Recent developments in Neural Radiance Fields (NeRF) have showcased notable progress in the synthesis of novel views. Nevertheless, there is limited research on inpainting 3D scenes using implicit representations. Traditional approaches utilizing ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '22: Proceedings of the 30th ACM International Conference on Multimedia

October 2022

7537 pages

ISBN:9781450392037

DOI:10.1145/3503161

General Chairs:
João Magalhães
NOVA University of Lisbon, Portugal
,
Alberto del Bimbo
University of Florence, Italy
,
Shin'ichi Satoh
National Institute of Informatics, Japan
,
Nicu Sebe
University of Trento, Italy
,
Program Chairs:
Xavier Alameda-Pineda
Inria, Grenoble, France
,
Qin Jin
Renmin University of China, China
,
Vincent Oria
New Jersey Institute of Technology, USA
,
Laura Toni
University College London, UK

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 October 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MM '22

Sponsor:

SIGMM

MM '22: The 30th ACM International Conference on Multimedia

October 10 - 14, 2022

Lisboa, Portugal

Acceptance Rates

Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

56
Total Citations
View Citations
888
Total Downloads

Downloads (Last 12 months)303
Downloads (Last 6 weeks)18

Reflects downloads up to 10 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Huang W(2025)Novel View Synthesis Based on Similar PerspectiveComputer Animation and Virtual Worlds10.1002/cav.7000636:1Online publication date: 7-Jan-2025
https://doi.org/10.1002/cav.70006
Jin PYu Z(2024)Research on 3D Visualization of Drone Scenes Based on Neural Radiance FieldsElectronics10.3390/electronics1309168213:9(1682)Online publication date: 26-Apr-2024
https://doi.org/10.3390/electronics13091682
Cai YLi RLiu L(2024)MV2MV: Multi-View Image Translation via View-Consistent Diffusion ModelsACM Transactions on Graphics10.1145/368797743:6(1-12)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687977
Sinha SKühn JGraf HWeinmann M(2024)SpectralSplatsViewer: An Interactive Web-Based Tool for Visualizing Cross-Spectral Gaussian SplatsProceedings of the 29th International ACM Conference on 3D Web Technology10.1145/3665318.3677151(1-10)Online publication date: 25-Sep-2024
https://dl.acm.org/doi/10.1145/3665318.3677151
Xu RLi GLi CYang ZLiu YChen MCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)OSNeRF: On-demand Semantic Neural Radiance Fields for Fast and Robust 3D Object ReconstructionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681686(4505-4514)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681686
Shi JJiang MLu MChen TCao XMa ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)HINER: Neural Representation for Hyperspectral ImageProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681643(9837-9846)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681643
Yan JPeng RTang LWang RCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time Rendering of Temporally Complex Dynamic ScenesProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681463(7871-7880)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681463
Wang YZhou KZhang WXiao CCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)MegaSurf: Scalable Large Scene Neural Surface ReconstructionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681303(6414-6423)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681303
Wang ZXie SPan CWang GCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)SpecGaussian with Latent Features: A High-quality Modeling of the View-dependent Appearance for 3D Gaussian SplattingProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3681059(6270-6278)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3681059
Zhang JChen KChen SZheng YHuang TYu ZCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)SpikeGS: 3D Gaussian Splatting from Spike Streams with High-Speed Camera MotionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680996(9194-9203)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680996
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten