Results 81 to 90 of about 2,209 (160)

Benchmarking Robustness in Neural Radiance Fields

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Neural Radiance Field (NeRF) has demonstrated excellent quality in novel view synthesis, thanks to its ability to model 3D object geometries in a concise formulation. However, current approaches to NeRF-based models rely on clean images with accurate camera calibration, which can be difficult to obtain in the real world, where data is often subject to ...
Wang, Chen, Wang, Angtian, Li, Junbo, Yuille, Alan, Xie, Cihang   +4 more
openaire   +2 more sources

A review of dynamic scene reconstruction based on neural representation

Dianxin kexue
Dynamic scene reconstruction holds significant research value in the fields of computer vision and virtual reality. Recent advancements in neural representation technologies have facilitated rapid progress in this task.
LU Ping, SUN Junjie, LI Wan, LIN Jiaxin, LIU Hong, LIU Hong, FENG Daquan   +6 more
doaj  

Res-NeuS: Deep Residuals and Neural Implicit Surface Learning for Multi-View Reconstruction

Sensors
Surface reconstruction using neural networks has proven effective in reconstructing dense 3D surfaces through image-based neural rendering. Nevertheless, current methods are challenging when dealing with the intricate details of large-scale scenes.
Wei Wang, Fengjiao Gao, Yongliang Shen
doaj   +1 more source

Neural Radiance Fields with Torch Units

Neural Radiance Fields (NeRF) give rise to learning-based 3D reconstruction methods widely used in industrial applications. Although prevalent methods achieve considerable improvements in small-scale scenes, accomplishing reconstruction in complex and large-scale scenes is still challenging.
Ni, Bingnan, Wang, Huanyu, Bai, Dongfeng, Weng, Minghe, Qi, Dexin, Qiu, Weichao, Liu, Bingbing   +6 more
openaire   +2 more sources

4D Facial Avatar Reconstruction From Monocular Video via Efficient and Controllable Neural Radiance Fields

IEEE Access
We present an efficient approach for monocular 4D facial avatar reconstruction using a dynamic neural radiance field (NeRF). Over the years, NeRFs have been popular methods for 3D scene representation, but lack computational efficiency and controllabilty,
Jeong-Gi Kwak, Hanseok Ko
doaj   +1 more source

DialogueNeRF: towards realistic avatar face-to-face conversation video generation

Visual Intelligence
Conversation is an essential component of virtual avatar activities in the metaverse. With the development of natural language processing, significant breakthroughs have been made in text and voice conversation generation.
Yichao Yan, Zanwei Zhou, Zi Wang, Jingnan Gao, Xiaokang Yang   +4 more
doaj   +1 more source

Loop Detection Method Based on Neural Radiance Field BoW Model for Visual Inertial Navigation of UAVs

Remote Sensing
The loop closure detection (LCD) methods in Unmanned Aerial Vehicle (UAV) Visual Inertial Navigation System (VINS) are often affected by issues such as insufficient image texture information and limited observational perspectives, resulting in ...
Xiaoyue Zhang, Yue Cui, Yanchao Ren, Guodong Duan, Huanrui Zhang   +4 more
doaj   +1 more source

Planar Reflection-Aware Neural Radiance Fields

SIGGRAPH Asia 2024 Conference Papers
Neural Radiance Fields (NeRF) have demonstrated exceptional capabilities in reconstructing complex scenes with high fidelity. However, NeRF's view dependency can only handle low-frequency reflections. It falls short when handling complex planar reflections, often interpreting them as erroneous scene geometries and leading to duplicated and inaccurate ...
Chen Gao, Yipeng Wang, Changil Kim, Jia-Bin Huang, Johannes Kopf   +4 more
openaire   +2 more sources

3D Rendering of Cystoscopy Video Footage: A Novel Method Utilizing Neural Radiance Field Processing. [PDF]

Cureus
Katz JE, Finegan J, Beutelspacher PF, Lu J, Lin S, Yip M, Sur RL.   +6 more
europepmc   +1 more source

PA-NeRF, a neural radiance field model for 3D photoacoustic tomography reconstruction from limited Bscan data. [PDF]

Biomed Opt Express
Zou Y, Lin Y, Zhu Q.
europepmc   +1 more source