Regularizing Neural Radiance Fields from Sparse Rgb-D Inputs

Qian Li; Franck Multon; Adnane Boukhayma

doi:10.1109/icip49359.2023.10222706

ScienceGate Book Chapters

JOURNAL ARTICLE

Regularizing Neural Radiance Fields from Sparse Rgb-D Inputs

Qian Li Franck Multon Adnane Boukhayma

Year: 2023 Pages: 2320-2324

DOI: 10.1109/icip49359.2023.10222706

Get Full-Text PDF Get Analytical Report

Abstract

International audience

Keywords:

Radiance Computer science Rendering (computer graphics) Regularization (linguistics) Exploit Artificial intelligence Deep neural networks Consistency (knowledge bases) Algorithm Artificial neural network Computer vision Remote sensing Geography

Metrics

Cited By

0.73

FWCI (Field Weighted Citation Impact)

Refs

0.67

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Advanced Vision and Imaging

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Computer Graphics and Visualization Techniques

Physical Sciences → Computer Science → Computer Graphics and Computer-Aided Design

3D Shape Modeling and Analysis

Physical Sciences → Engineering → Computational Mechanics

Regularizing Neural Radiance Fields from Sparse Rgb-D Inputs

Abstract

Metrics

Citation History

Topics

Related Documents

Regularizing Neural Radiance Fields from Sparse RGD Inputs

RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs

Neural Radiance Fields From Sparse RGB-D Images for High-Quality View Synthesis

NeRF-OR: neural radiance fields for operating room scene reconstruction from sparse-view RGB-D videos

Where and How: Mitigating Confusion in Neural Radiance Fields from Sparse Inputs