Results 31 to 40 of about 2,209 (160)

Reconstructing Continuous Light Field From Single Coded Image

IEEE Access, 2023
We propose a method for reconstructing a continuous light field of a target scene from a single observed image. Our method takes the best of two worlds: joint aperture-exposure coding for compressive light-field acquisition, and a neural radiance field ...
Yuya Ishikawa, Keita Takahashi, Chihiro Tsutake, Toshiaki Fujii   +3 more
doaj   +1 more source

Generative Neural Articulated Radiance Fields

, 2022
Unsupervised learning of 3D-aware generative adversarial networks (GANs) using only collections of single-view 2D photographs has very recently made much progress. These 3D GANs, however, have not been demonstrated for human bodies and the generated radiance fields of existing frameworks are not directly editable, limiting their applicability in ...
Bergman, Alexander W. (author), Kellnhofer, P. (author), Yifan, Wang (author), Chan, Eric R. (author), Lindell, David B. (author), Wetzstein, Gordon (author)   +5 more
openaire   +3 more sources

Unmanned Aerial Vehicle-Neural Radiance Field (UAV-NeRF): Learning Multiview Drone Three-Dimensional Reconstruction with Neural Radiance Field

Remote Sensing
In traditional 3D reconstruction using UAV images, only radiance information, which is treated as a geometric constraint, is used in feature matching, allowing for the restoration of the scene’s structure. After introducing radiance supervision, NeRF can
Li Li, Yongsheng Zhang, Zhipeng Jiang, Ziquan Wang, Lei Zhang, Han Gao   +5 more
doaj   +1 more source

NeRFlex: Flexible Neural Radiance Fields With Diffeomorphic Deformation

IEEE Access
Due to the vast array of NeRF-based techniques, the representation power of Neural Radiance Fields (NeRF) has been quickly rising in recent years. However, it is still difficult to offer fresh perspectives for user-controlled geometry alterations with ...
Jiyoon Shin, Sangwoo Hong, Jungwoo Lee
doaj   +1 more source

MaRF: Representing Mars as Neural Radiance Fields

, 2023
The aim of this work is to introduce MaRF, a novel framework able to synthesize the Martian environment using several collections of images from rover cameras. The idea is to generate a 3D scene of Mars' surface to address key challenges in planetary surface exploration such as: planetary geology, simulated navigation and shape analysis. Although there
Giusti, Lorenzo, Garcia, Josue, Cozine, Steven, Suen, Darrick, Nguyen, Christina, Alimo, Ryan   +5 more
openaire   +3 more sources

Plenoxels: Radiance Fields without Neural Networks

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
For video and code, please see https://alexyu.net ...
Yu, Alex, Fridovich-Keil, Sara, Tancik, Matthew, Chen, Qinhong, Recht, Benjamin, Kanazawa, Angjoo   +5 more
openaire   +2 more sources

Removing Objects From Neural Radiance Fields

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023
Neural Radiance Fields (NeRFs) are emerging as a ubiquitous scene representation that allows for novel view synthesis. Increasingly, NeRFs will be shareable with other people. Before sharing a NeRF, though, it might be desirable to remove personal information or unsightly objects.
Weder, Silvan, Garcia-Hernando, Guillermo, Monszpart, Aron, Pollefeys, Marc, Brostow, Gabriel, Firman, Michael, Vicente, Sara   +6 more
openaire   +2 more sources

Impact of Rain on 3D Reconstruction with Multi-View Stereo, Neural Radiance Fields and Gaussian Splatting [PDF]

ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Image-based 3D reconstruction uncovers many applications in documenting the geometry of the environment. Nonetheless, the assumption that images are captured in clear air rarely holds in real-world settings where adverse weather conditions are inevitable.
I. Petrovska, B. Jutzi
doaj   +1 more source

VMRF: View Matching Neural Radiance Fields

Proceedings of the 30th ACM International Conference on Multimedia, 2022
Neural Radiance Fields (NeRF) have demonstrated very impressive performance in novel view synthesis via implicitly modelling 3D representations from multi-view 2D images. However, most existing studies train NeRF models with either reasonable camera pose initialization or manually-crafted camera pose distributions which are often unavailable or hard to
Zhang, Jiahui, Zhan, Fangneng, Wu, Rongliang, Yu, Yingchen, Zhang, Wenqing, Song, Bai, Zhang, Xiaoqin, Lu, Shijian   +7 more
openaire   +2 more sources

Hyperspectral Neural Radiance Field Method Based on Reference Spectrum

IEEE Access
The Neural Radiance Field (NeRF) method for datasets is gaining attention for its wide applications and research value. Hyperspectral images have also gained many applications in recent years.
Runchuan Ma, Sailing He
doaj   +1 more source