Results 11 to 20 of about 4,408 (223)
Strata-NeRF : Neural Radiance Fields for Stratified Scenes
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 Neural Radiance Field (NeRF) approaches learn the underlying 3D representation of a scene and generate photo-realistic novel views with high fidelity. However, most proposed settings concentrate on modelling a single object or a single level of a scene ...
Babu, R Venkatesh +6 more
core +2 more sources
DoF-NeRF: Depth-of-Field Meets Neural Radiance Fields
Proceedings of the 30th ACM International Conference on Multimedia, 2022 Neural Radiance Field (NeRF) and its variants have exhibited great success on representing 3D scenes and synthesizing photo-realistic novel views. However, they are generally based on the pinhole camera model and assume all-in-focus inputs.
Cao, Zhiguo +5 more
core +2 more sources
D-NeRF: Neural Radiance Fields for Dynamic Scenes [PDF]
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 Neural rendering techniques combining machine learning with geometric reasoning have arisen as one of the most promising approaches for synthesizing novel views of a scene from a sparse set of images. Among these, stands out the Neural radiance fields (NeRF), which trains a deep network to map 5D input coordinates (representing spatial location and ...
Pumarola Peris, Albert +3 more
openaire +5 more sources
NeRF-Editing: Geometry Editing of Neural Radiance Fields [PDF]
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Accepted by CVPR ...
Yu-Jie Yuan +5 more
openaire +2 more sources
Spec-NeRF: Multi-Spectral Neural Radiance Fields
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023 <p>Spec-NeRF jointly optimizes the degradation parameters and achieves high-quality multi-spectral image reconstruction results at novel views, which only requires a low-cost camera (like a phone camera but in RAW mode) and several off-the-shelf color filters. We also provide real scenarios and synthetic datasets for related studies.
Jiabao Li +4 more
openaire +2 more sources
DReg-NeRF: Deep Registration for Neural Radiance Fields
2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023 Accepted at ICCV ...
Yu Chen, Gim Hee Lee
openaire +2 more sources
Ultra-NeRF: Neural Radiance Fields for Ultrasound Imaging
CoRR, 2023 We present a physics-enhanced implicit neural representation (INR) for ultrasound (US) imaging that learns tissue properties from overlapping US sweeps. Our proposed method leverages a ray-tracing-based neural rendering for novel view US synthesis. Recent publications demonstrated that INR models could encode a representation of a three-dimensional ...
Magdalena Wysocki +5 more
openaire +3 more sources
Point-NeRF: Point-based Neural Radiance Fields
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022 Accepted to CVPR 2022 (Oral)
Qiangeng Xu +6 more
openaire +2 more sources
Communications of the ACM, 2020 We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully connected (nonconvolutional) deep network, whose input is a single continuous 5D ...
Ben Mildenhall +5 more
openaire +3 more sources
S-NeRF: Neural Radiance Fields for Street Views
CoRR, 2023 Neural Radiance Fields (NeRFs) aim to synthesize novel views of objects and scenes, given the object-centric camera views with large overlaps. However, we conjugate that this paradigm does not fit the nature of the street views that are collected by many self-driving cars from the large-scale unbounded scenes.
Ziyang Xie +4 more
openaire +3 more sources