Results 101 to 110 of about 4,408 (223)

SurfelNeRF: Neural Surfel Radiance Fields for Online Photorealistic Reconstruction of Indoor Scenes

, 2023
Online reconstructing and rendering of large-scale indoor scenes is a long-standing challenge. SLAM-based methods can reconstruct 3D scene geometry progressively in real time but can not render photorealistic results.
Cao, Yan-Pei, Gao, Yiming, Shan, Ying
core  

IE-NeRF: Inpainting Enhanced Neural Radiance Fields in the Wild

CoRR
We present a novel approach for synthesizing realistic novel views using Neural Radiance Fields (NeRF) with uncontrolled photos in the wild. While NeRF has shown impressive results in controlled settings, it struggles with transient objects commonly found in dynamic and time-varying scenes.
Shuaixian Wang, Haoran Xu 0004, Yaokun Li, Jiwei Chen, Guang Tan   +4 more
openaire   +2 more sources

Ortho-3DGS: True Digital Orthophoto Generation From Unmanned Aerial Vehicle Imagery Using the Depth-Regulated 3D Gaussian Splatting

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
True digital orthophoto maps (DOMs) are vital spatial data sources due to their high precision, detail, and accessibility. However, traditional generation methods using image differential correction with DEM/DSM often produce significant distortions from
Junxing Yang, Zhenglong Cai, Tianjiao Wang, Tong Ye, Haoran Gao, He Huang   +5 more
doaj   +1 more source

NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review

, 2022
Neural Radiance Field (NeRF), a new novel view synthesis with implicit scene representation has taken the field of Computer Vision by storm. As a novel view synthesis and 3D reconstruction method, NeRF models find applications in robotics, urban mapping,
Gao, Kyle, Gao, Yina, He, Hongjie, Li, Jonathan, Lu, Denning, Xu, Linlin   +5 more
core  

Evaluating Neural Radiance Fields for ADA-Compliant Sidewalk Assessments: A Comparative Study with LiDAR and Manual Methods

Infrastructures
An accurate assessment of sidewalk conditions is critical for ensuring compliance with the Americans with Disabilities Act (ADA), particularly to safeguard mobility for wheelchair users.
Hang Du, Shuaizhou Wang, Linlin Zhang, Mark Amo-Boateng, Yaw Adu-Gyamfi   +4 more
doaj   +1 more source

Editing Implicit and Explicit Representations of Radiance Fields: A Survey [PDF]

Neural Radiance Fields (NeRF) revolutionized novel view synthesis in recent years by offering a new volumetric representation, which is compact and provides high-quality image rendering.
ELGHAZALY, Gamal, FRANK, Raphaël, HUBERT, Arthur   +2 more
core  

FisherRF: Active View Selection and Uncertainty Quantification for Radiance Fields using Fisher Information

, 2023
This study addresses the challenging problem of active view selection and uncertainty quantification within the domain of Radiance Fields. Neural Radiance Fields (NeRF) have greatly advanced image rendering and reconstruction, but the limited ...
Daniilidis, Kostas, Jiang, Wen, Lei, Boshu   +2 more
core  

Fast Non-Rigid Radiance Fields from Monocularized Data [PDF]

, 2022
3D reconstruction and novel view synthesis of dynamic scenes from collectionsof single views recently gained increased attention. Existing work showsimpressive results for synthetic setups and forward-facing real-world data, butis severely limited in the
Castillo, S., Golyanik, V., Kappel, M., Magnor, M., Theobalt, C.   +4 more
core   +1 more source

Pose-Free Neural Radiance Fields via Implicit Pose Regularization

, 2023
Pose-free neural radiance fields (NeRF) aim to train NeRF with unposed multi-view images and it has achieved very impressive success in recent years. Most existing works share the pipeline of training a coarse pose estimator with rendered images at first,
Liu, Kunhao, Lu, Shijian, Shao, Ling, Wu, Rongliang, Yu, Yingchen, Zhan, Fangneng, Zhang, Jiahui, Zhang, Xiaoqin   +7 more
core  

UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields

CoRR
Neural Radiance Field (NeRF)-based segmentation methods focus on object semantics and rely solely on RGB data, lacking intrinsic material properties. This limitation restricts accurate material perception, which is crucial for robotics, augmented reality, simulation, and other applications.
Fabian Perez, Sara Rojas 0001, Carlos Hinojosa, Hoover Rueda-Chacón, Bernard Ghanem   +4 more
openaire   +2 more sources