计算机视觉|2021年必读的10 个计算机视觉论文总结( 二 )

CityNeRF: Building NeRF at City Scale [10
该模型称为 CityNeRF ，是从 NeRF 发展而来的，NeRF 是最早使用辐射场和机器学习从图像构建 3D 模型的模型之一。但 NeRF 效率不高而且只适用于单一规模。在这里， CityNeRF 同时应用于卫星和地面图像，生成各种 3D 模型。简而言之他们将 NeRF 带入了城市规模。
引用[1
A. Ramesh et al. Zero-shot text-to-image generation 2021. arXiv:2102.12092
[2
Taming Transformers for High-Resolution Image Synthesis Esser et al. 2020.
[3
Liu Z. et al. 2021 “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows” arXiv preprint
[bonus
Yuille A.L. and Liu C. 2021. Deep nets: What have they ever done for vision?. International Journal of Computer Vision 129(3) pp.781–802
[4
Liu A. Tucker R. Jampani V. Makadia A. Snavely N. and Kanazawa A. 2020. Infinite Nature: Perpetual View Generation of Natural Scenes from a Single Image
[5
Pandey et al. 2021 Total Relighting: Learning to Relight Portraits for Background Replacement doi: 10.1145/3450626.3459872
[6
Holynski Aleksander et al. “Animating Pictures with Eulerian Motion Fields.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021.
[7
Michael Niemeyer and Andreas Geiger (2021) “GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields” Published in CVPR 2021.
[8
Stepan Tulyakov Daniel Gehrig Stamatios Georgoulis Julius Erbach Mathias Gehrig Yuanyou Li Davide Scaramuzza TimeLens: Event-based Video Frame Interpolation IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Nashville 2021
[9
a) CLIPDraw: exploring text-to-drawing synthesis through language-image encoders
b) StyleCLIPDraw: Schaldenbrand P. Liu Z. and Oh J. 2021. StyleCLIPDraw: Coupling Content and Style in Text-to-Drawing Synthesis.
[10
Xiangli Y. Xu L. Pan X. Zhao N. Rao A. Theobalt C. Dai B. and Lin D. 2021. CityNeRF: Building NeRF at City Scale.
原文地址：https://www.overfit.cn/post/e04755fce41e4db2959acfe688a7e3ac
本文作者：Louis Bouchard