Results 111 to 120 of about 4,408 (223)

NeRF-Art: Text-Driven Neural Radiance Fields Stylization

IEEE Transactions on Visualization and Computer Graphics
As a powerful representation of 3D scenes, the neural radiance field (NeRF) enables high-quality novel view synthesis from multi-view images. Stylizing NeRF, however, remains challenging, especially on simulating a text-guided style with both the appearance and the geometry altered simultaneously.
Can Wang 0007, Ruixiang Jiang, Menglei Chai, Mingming He, Dongdong Chen 0001, Jing Liao 0001   +5 more
openaire   +3 more sources

Removing Adverse Volumetric Effects From Trained Neural Radiance Fields

, 2023
While the use of neural radiance fields (NeRFs) in different challenging settings has been explored, only very recently have there been any contributions that focus on the use of NeRF in foggy environments.
Hamran, Victor P., Mester, Rudolf, Skui, Vegard, Stahl, Annette, Teigen, Andreas L., Yip, Mauhing   +5 more
core  

Neural Radiance Fields for Fisheye Driving Scenes Using Edge-Aware Integrated Depth Supervision

Sensors
Neural radiance fields (NeRF) have become an effective method for encoding scenes into neural representations, allowing for the synthesis of photorealistic views of unseen views from given input images.
Jiho Choi, Sang Jun Lee
doaj   +1 more source

VRS-NeRF: Visual Relocalization with Sparse Neural Radiance Field

source code https://github.com/feixue94/vrs ...
Fei Xue, Ignas Budvytis, Daniel Olmeda Reino, Roberto Cipolla   +3 more
openaire   +2 more sources

Real-time facial reconstruction and expression replacement based on neural radiation field

Systems and Soft Computing
It is now possible to do high-fidelity 3D facial reconstruction and unique view synthesis thanks to the recent discovery of Neural Radiance Fields (NeRF), which has established its substantial importance in the field of 3D vision.
Shenning Zhang, Hui Li, Xuefeng Tian
doaj   +1 more source

Fast Learning Radiance Fields by Shooting Much Fewer Rays

, 2023
Learning radiance fields has shown remarkable results for novel view synthesis. The learning procedure usually costs lots of time, which motivates the latest methods to speed up the learning procedure by learning without neural networks or using more ...
Han, Zhizhong, Liu, Yu-Shen, Shi, Kanle, Xing, Ruofan, Zeng, Yunfan, Zhang, Wenyuan   +5 more
core  

The NeRF Signature: Codebook-Aided Watermarking for Neural Radiance Fields

IEEE Transactions on Pattern Analysis and Machine Intelligence
16 pages, accepted by ...
Ziyuan Luo, Anderson Rocha 0001, Boxin Shi, Qing Guo 0005, Haoliang Li, Renjie Wan   +5 more
openaire   +3 more sources

CaesarNeRF: Calibrated Semantic Representation for Few-shot Generalizable Neural Rendering

, 2023
Generalizability and few-shot learning are key challenges in Neural Radiance Fields (NeRF), often due to the lack of a holistic understanding in pixel-level rendering.
Chen, Tianyi, Ding, Tianyu, Liang, Luming, Nevatia, Ram, Zharkov, Ilya, Zhu, Haidong   +5 more
core  

Rugularizing generalizable neural radiance field with limited-view images

Complex & Intelligent Systems
We present a novel learning model with attention and prior guidance for view synthesis. In contrast to previous works that focus on optimizing for specific scenes with densely captured views, our model explores a generic deep neural framework to ...
Wei Sun, Ruijia Cui, Qianzhou Wang, Xianguang Kong, Yanning Zhang   +4 more
doaj   +1 more source

Multispectral-NeRF: A Multispectral Modeling Approach Based on Neural Radiance Fields

Applied Sciences
3D reconstruction technology generates three-dimensional representations of real-world objects, scenes, or environments using sensor data such as 2D images, with extensive applications in robotics, autonomous vehicles, and virtual reality systems ...
Hong Zhang, Fei Guo, Zihan Xie, Dizhao Yao   +3 more
doaj   +1 more source