Results 61 to 70 of about 10,601 (230)

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

PBR‐Inspired Controllable Diffusion for Image Generation

Computer Graphics Forum, EarlyView.
Abstract Despite recent advances in text‐to‐image generation, controlling geometric layout and PBR material properties in synthesized scenes remains challenging. We present a pipeline that first produces a G‐buffer (albedo, normals, depth, roughness, shading, and metallic) from a text prompt and then renders a final image through a PBR‐inspired branch ...
Bowen Xue, Giuseppe Claudio Guarnera, Shuang Zhao, Zahra Montazeri   +3 more
wiley   +1 more source

LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields [PDF]

, 2023
We introduce a new task, novel view synthesis for LiDAR sensors. While traditional model-based LiDAR simulators with style-transfer neural networks can be applied to render novel views, they fall short of producing accurate and realistic LiDAR patterns ...
Salzmann, Mathieu, Tao, Tang, Lao, Yixing, Chen, Peng, Gao, Longfei, Wang, Guangrun, Liang, Xiaodan, Zhao, Hengshuang, Yu, Kaicheng, Hao, Dayang   +9 more
core  

Neural Radiance Fields with Hash-Low-Rank Decomposition

Applied Sciences
In recent advancements in novel view synthesis and neural rendering, neural radiance field (NeRF) has emerged as a powerful technique for synthesizing high-quality novel views of complex 3D scenes.
Jiaxin Wang, Weichen Dai, Kangcheng Ma, Wanzeng Kong   +3 more
doaj   +1 more source

NeRF 3D Reconstruction Method Based on Cone Tracking and Network Decomposition [PDF]

Jisuanji gongcheng
In computer vision, Neural Radiance Fields (NeRF) define processes that use spatial coordinates or other dimensions, such as time and camera pose, as input and simulate the objective function through a Multi-Layer Perceptron (MLP) network to generate the
JING Weipeng, WANG Yuanfeng, LI Chao
doaj   +1 more source

VF-NeRF: Viewshed Fields for Rigid NeRF Registration

3D scene registration is a fundamental problem in computer vision that seeks the best 6-DoF alignment between two scenes. This problem was extensively investigated in the case of point clouds and meshes, but there has been relatively limited work regarding Neural Radiance Fields (NeRF).
Leo Segre, Shai Avidan
openaire   +3 more sources

Sync-NeRF: Generalizing Dynamic NeRFs to Unsynchronized Videos

Proceedings of the AAAI Conference on Artificial Intelligence
Recent advancements in 4D scene reconstruction using neural radiance fields (NeRF) have demonstrated the ability to represent dynamic scenes from multi-view videos. However, they fail to reconstruct the dynamic scenes and struggle to fit even the training views in unsynchronized settings.
Seoha Kim, Jeongmin Bae 0001, Youngsik Yun, Hahyun Lee, Gun Bang, Youngjung Uh   +5 more
openaire   +3 more sources

A Real‐Time Multi‐Scale Neural Representation for Complex Surface Reflectance

Computer Graphics Forum, EarlyView.
Abstract Recent machine learning methods have significantly advanced the state of the art in the classic problem of representing surface appearance over angle, space, and scale. The models tend, however, to be relatively heavy compared to traditional fixed‐function representations, making real‐time application challenging.
Heikki Timonen, Pauli Kemppinen, Jaakko Lehtinen   +2 more
wiley   +1 more source

Comparative Assessment of Neural Radiance Fields and Photogrammetry in Digital Heritage: Impact of Varying Image Conditions on 3D Reconstruction

Remote Sensing
This paper conducts a comparative evaluation between Neural Radiance Fields (NeRF) and photogrammetry for 3D reconstruction in the cultural heritage domain.
Valeria Croce, Dario Billi, Gabriella Caroti, Andrea Piemonte, Livio De Luca, Philippe Véron   +5 more
doaj   +1 more source

Multi‐Spectral Gaussian Splatting with Neural Color Representation

Computer Graphics Forum, EarlyView.
Abstract 3D Gaussian Splatting (3DGS) [KKLD23] has transformed novel‐view synthesis from RGB images, yet remains restricted to the visible spectrum. Many applications, including agricultural monitoring, rely on multi‐spectral imaging, where spectral camera alignment and scalability pose major challenges.
Lukas Meyer, Josef Grün, Maximilian Weiherer, Bernhard Egger, Marc Stamminger, Linus Franke   +5 more
wiley   +1 more source