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WaterHE-NeRF: Water-Ray Matching Neural Radiance Fields for Underwater Scene Reconstruction

Information Fusion
Dehuan Zhang   +2 more
exaly  

Bayesian Uncertainty Analysis for Underwater 3D Reconstruction With Neural Radiance Fields

Applied Mathematical Modelling
Haojie Lian, Leilei Chen, Yilin Qu
exaly  

NeRFMeshing: Distilling Neural Radiance Fields Into Geometrically-Accurate 3D Meshes

Federico Tombari   +2 more
exaly  

Eigengrasp-Conditioned Neural Radiance Fields

open access: yesIEEE Access, 2023
In this study, we address the problem of learning a posture-controllable three-dimensional (3D) representation of articulated robotic hands. Neural radiance fields (NeRFs) have outperformed grid-like 3D representations on a novel view synthesis tasks ...
Hiroaki Aizawa, Itoshi Naramura
doaj   +3 more sources

Generative Neural Articulated Radiance Fields

open access: yesAdvances in Neural Information Processing Systems 35, 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)   +5 more
openaire   +6 more sources

Cross-Spectral Neural Radiance Fields

open access: yes2022 International Conference on 3D Vision (3DV), 2022
We propose X-NeRF, a novel method to learn a Cross-Spectral scene representation given images captured from cameras with different light spectrum sensitivity, based on the Neural Radiance Fields formulation. X-NeRF optimizes camera poses across spectra during training and exploits Normalized Cross-Device Coordinates (NXDC) to render images of different
Matteo Poggi   +5 more
openaire   +6 more sources

Neural Radiance Fields for High-Fidelity Soft Tissue Reconstruction in Endoscopy [PDF]

open access: yesSensors
The advancement of neural radiance fields (NeRFs) has facilitated the high-quality 3D reconstruction of complex scenes. However, for most NeRFs, reconstructing 3D tissues from endoscopy images poses significant challenges due to the occlusion of soft ...
Jinhua Liu   +3 more
doaj   +2 more sources

NeRFReN: Neural Radiance Fields with Reflections

open access: yes2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
Neural Radiance Fields (NeRF) has achieved unprecedented view synthesis quality using coordinate-based neural scene representations. However, NeRF's view dependency can only handle simple reflections like highlights but cannot deal with complex reflections such as those from glass and mirrors.
Yuan-Chen Guo   +4 more
openaire   +4 more sources

NeRFlex: Flexible Neural Radiance Fields With Diffeomorphic Deformation

open access: yesIEEE 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   +2 more sources

Neural radiance fields assisted by image features for UAV scene reconstruction [PDF]

open access: yesScientific Reports
With the rapid advancement of Unmanned Aerial Vehicle applications, vision-based 3D scene reconstruction has demonstrated significant value in fields such as remote sensing and target detection.
Zhihong Chen   +4 more
doaj   +2 more sources

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