Vol. 5 No. 1 (2025): Issue 5
Articles

Real-Time 3D Model Reconstruction using Gaussian Mixture Model

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  • Ella Thompson,
  • Marcus Liu,
  • Sophia Patel,
  • Haibo Wang
Ella Thompson
Center for Computational Imaging, Boise State University
Marcus Liu
Institute for 3D Vision and Graphics, University of New Mexico
Sophia Patel
Applied Computational Research Institute, University of Central Oklahoma
Haibo Wang
Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, 15213, USA

Published 2025-03-02

Keywords

  • 3D Model,
  • Reconstruction,
  • Gaussian,
  • Mixture,
  • Model

How to Cite

Thompson, E., Liu, M., Patel, S., & Wang, H. (2025). Real-Time 3D Model Reconstruction using Gaussian Mixture Model. Generative Artificial Intelligence Research, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/GAIR/article/view/104

Abstract

This paper addresses the urgent need for real-time 3D model reconstruction in various fields such as computer vision, robotics, and virtual reality. The current research landscape faces significant challenges in achieving accurate and efficient 3D reconstruction due to the complex nature of real-world environments and the computational demands of processing large amounts of data. In light of these challenges, this study proposes a novel approach based on utilizing Gaussian Mixture Model to improve the real-time 3D model reconstruction process. The innovative method combines the power of statistical modeling with real-time processing capabilities to enhance the accuracy and speed of 3D reconstruction. By presenting this new solution, this paper contributes to advancing the state-of-the-art in the field of real-time 3D model reconstruction, offering a promising direction for future research and applications.

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