Results 21 to 30 of about 16,932 (278)

CoNeRF: Controllable Neural Radiance Fields

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022
We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which part of the scene one wishes to control with just a small number of mask annotations in the training images. Our key idea is to treat the attributes as latent variables that are
Kania, Kacper, Yi, Kwang Moo, Kowalski, Marek, Trzciński, Tomasz, Tagliasacchi, Andrea   +4 more
openaire   +2 more sources

Cross-Spectral Neural Radiance Fields

2022 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, Pierluigi Zama Ramirez, Fabio Tosi, Samuele Salti, Stefano Mattoccia, Luigi Di Stefano   +5 more
openaire   +3 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, Corona Puyane, Enric, Pons-Moll, Gerard, Moreno-Noguer, Francesc   +3 more
openaire   +5 more sources

Eigengrasp-Conditioned Neural Radiance Fields

IEEE 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; however, rendering novel views while controlling the joints and posture of robotic hands using NeRFs ...
Hiroaki Aizawa, Itoshi Naramura
openaire   +2 more sources

Extending stochastic resonance for neuron models to general Levy noise [PDF]

, 2009
A recent paper by Patel and Kosko (2008) demonstrated stochastic resonance (SR) for general feedback continuous and spiking neuron models using additive Levy noise constrained to have finite second moments. In this brief, we drop this constraint and show
Applebaum, D.
core   +1 more source

Locally Stylized Neural Radiance Fields

2023 IEEE/CVF International Conference on Computer Vision (ICCV), 2023
ICCV ...
Pang, Hong-Wing, Hua, Binh-Son, Yeung, Sai-Kit   +2 more
openaire   +2 more sources

Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data [PDF]

, 2010
The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1.
C Hu, CD Mobley, D Doxaran, D Doxaran, F Shen, F Shen, Fang Shen, H Claustre, HR Gordon, HR Gordon, HT Shen, J Zhang, JF Li, KL Carder, M Fettweis, Mhd. Suhyb Salama, P Kubelka, Q He, R Doerffer, RL Miller, S Dasgupta, SL Chen, SL Yang, Th Schroeder, V Krivtsov, W Verhoef, Wouter Verhoef, Xiaoli Liu, Yunxuan Zhou, YZ Kong, ZP Lee, ZP Lee   +31 more
core   +4 more sources

Surgical neural radiance fields from one image. [PDF]

Int J Comput Assist Radiol Surg
Purpose: Neural Radiance Fields (NeRF) offer exceptional capabilities for 3D reconstruction and view synthesis, yet their reliance on extensive multi-view data limits their application in surgical intraoperative settings where only limited data is available.
Neri A, Fehrentz M, Penza V, Mattos LS, Haouchine N.   +4 more
europepmc   +3 more sources

MarkNerf:Watermarking for Neural Radiance Field

, 2023
A watermarking algorithm is proposed in this paper to address the copyright protection issue of implicit 3D models. The algorithm involves embedding watermarks into the images in the training set through an embedding network, and subsequently utilizing the NeRF model for 3D modeling.
Chen, Lifeng, Liu, Jia, Ke, Yan, Sun, Wenquan, Dong, Weina, Pan, Xiaozhong   +5 more
openaire   +2 more sources

Self-Evolving Neural Radiance Fields

, 2023
Recently, neural radiance field (NeRF) has shown remarkable performance in novel view synthesis and 3D reconstruction. However, it still requires abundant high-quality images, limiting its applicability in real-world scenarios. To overcome this limitation, recent works have focused on training NeRF only with sparse viewpoints by giving additional ...
Jung, Jaewoo, Han, Jisang, Kang, Jiwon, Kim, Seongchan, Kwak, Min-Seop, Kim, Seungryong   +5 more
openaire   +2 more sources