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NeRF explored: a comprehensive analysis of neural radiance field in 3D vision
Author(s)
M. Haider School of Electrical Engineering and Computer Sciences,NUST, Islamabad,Pakistan
Khurram Shahzad School of Electrical Engineering and Computer Sciences,NUST, Islamabad,Pakistan
Ammad Ullah Rameez School of Electrical Engineering and Computer Sciences,NUST, Islamabad,Pakistan
Sajid Umair University of Missouri Kansas City (UMKC),,USA
Safdar Abbas Khan School of Electrical Engineering and Computer Sciences,NUST, Islamabad,Pakistan
Abstract
Neural Radiance Fields (NeRF) refers to a method in computer vision that uses the power of Neural Networks that synthesize threedimensional scenes from two-dimensional images. They enable the creation of detailed and realistic images after modeling threedimensional scene as a function that predicts color and opacity at any given point in space. This allows the creation of new views from any desired point. NeRFs are therefore very useful in virtual/ augmented reality and computer-generated imagery. Using Neural Networks, NeRFs achieve unparalleled fidelity showing its importance in technology innovation. This paper provides an overview of NeRF highlighting its evolution and uses in diverse applications across various domains such as robotics navigation, urban reconstruction, human face generation, and many more. Our work provides readers with a systematic examination of NeRF's advancements by delving into different approaches and architecture used in the literature and will provide key insights to both novice and seasoned researchers in this dynamic field of computer vision.
Publication Details
Page(s) 1-1
DOI DOI not available
Published Journal: Second International Conference on Computing Technologies, Tools and Applications (ICTAPP-24), June 4-6,2024 (Abstract Book), Volume: 0, Issue: 0, Year: 2024
Keywords
deep learning Neural Networks NeRF Neural Radiance Field View Synthesis MultiLayer Perceptron
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