Super-NeRF: View-consistent Detail Generation for NeRF super-resolution
暂无分享,去创建一个
Qionghai Dai | Yuwang Wang | Tao Yu | Yuqi Han | Xiaohang Yu | Xiaohang Yu
[1] L. Gool,et al. DiffIR: Efficient Diffusion Model for Image Restoration , 2023, ArXiv.
[2] David J. Fleet,et al. Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild , 2023, ArXiv.
[3] Clayton D. Scott,et al. IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[4] Ben Poole,et al. DreamFusion: Text-to-3D using 2D Diffusion , 2022, ICLR.
[5] Yue Liu,et al. UPST-NeRF: Universal Photorealistic Style Transfer of Neural Radiance Fields for 3D Scene , 2022, ArXiv.
[6] Thu Nguyen-Phuoc,et al. SNeRF , 2022, ACM Trans. Graph..
[7] Yu-Kun Lai,et al. StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Yu-Kun Lai,et al. NeRF-Editing: Geometry Editing of Neural Radiance Fields , 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Andreas Geiger,et al. TensoRF: Tensorial Radiance Fields , 2022, ECCV.
[10] T. Müller,et al. Instant neural graphics primitives with a multiresolution hash encoding , 2022, ACM Trans. Graph..
[11] Yu-Wing Tai,et al. NeRF-SR: High Quality Neural Radiance Fields using Supersampling , 2021, ACM Multimedia.
[12] Lianghao Han,et al. A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-Resolution , 2021, IEEE Transactions on Geoscience and Remote Sensing.
[13] Christian Theobalt,et al. StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis , 2021, ICLR.
[14] David J. Fleet,et al. Cascaded Diffusion Models for High Fidelity Image Generation , 2021, J. Mach. Learn. Res..
[15] Qi Li,et al. SRDiff: Single Image Super-Resolution with Diffusion Probabilistic Models , 2021, Neurocomputing.
[16] Lourdes Agapito,et al. CodeNeRF: Disentangled Neural Radiance Fields for Object Categories , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[17] Jonathan T. Barron,et al. HyperNeRF , 2021, ACM Trans. Graph..
[18] Hung-Yu Tseng,et al. Learning to Stylize Novel Views , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[19] Ming-Yu Liu,et al. GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[20] Antonio Torralba,et al. BARF: Bundle-Adjusting Neural Radiance Fields , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[21] Nitish Srivastava,et al. Unconstrained Scene Generation with Locally Conditioned Radiance Fields , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[22] Changhu Wang,et al. MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[23] Pratul P. Srinivasan,et al. Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields , 2021, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[24] C. R. A. Chaitanya,et al. DONeRF: Towards Real‐Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks , 2021, Comput. Graph. Forum.
[25] M. Zollhöfer,et al. Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video , 2020, 2021 IEEE/CVF International Conference on Computer Vision (ICCV).
[26] Angjoo Kanazawa,et al. pixelNeRF: Neural Radiance Fields from One or Few Images , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Jiajun Wu,et al. pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Francesc Moreno-Noguer,et al. D-NeRF: Neural Radiance Fields for Dynamic Scenes , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Andreas Geiger,et al. GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Jonathan T. Barron,et al. NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Andreas Geiger,et al. GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis , 2020, NeurIPS.
[32] L. Gool,et al. SRFlow: Learning the Super-Resolution Space with Normalizing Flow , 2020, ECCV.
[33] Marcel C. Bühler,et al. DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution , 2020, ACCV.
[34] Ruigang Yang,et al. FaceScape: A Large-Scale High Quality 3D Face Dataset and Detailed Riggable 3D Face Prediction , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[35] Pratul P. Srinivasan,et al. NeRF , 2020, ECCV.
[36] C. Rudin,et al. PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[37] T. Michaeli,et al. Explorable Super Resolution , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[38] Paul Debevec,et al. DeepView: View Synthesis With Learned Gradient Descent , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Ravi Ramamoorthi,et al. Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines , 2019 .
[40] Sebastian Nowozin,et al. Occupancy Networks: Learning 3D Reconstruction in Function Space , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Chao Dong,et al. Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[42] Alexei A. Efros,et al. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[43] Bernhard Schölkopf,et al. EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[44] Christian Ledig,et al. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[45] Ting-Chun Wang,et al. Learning-based view synthesis for light field cameras , 2016, ACM Trans. Graph..
[46] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[47] Xiaoou Tang,et al. Learning a Deep Convolutional Network for Image Super-Resolution , 2014, ECCV.
[48] Alan C. Bovik,et al. Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.
[49] Kwang In Kim,et al. Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[50] H. Shum,et al. Image super-resolution using gradient profile prior , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.