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

Multiple View Geometry Construction with K-Singular Value Decomposition

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  • Zahra Mohammadi,
  • Leila Farahani
Zahra Mohammadi
Faculty of Advanced Engineering, University of Kurdistan
Leila Farahani
Institute of Applied Science Research, University of Lorestan

Published 2025-02-22

Keywords

  • Multiple View Geometry,
  • 3D Reconstruction,
  • Camera Parameters,
  • K-Singular Value Decomposition,
  • Structure-from-Motion

How to Cite

Mohammadi, Z., & Farahani, L. (2025). Multiple View Geometry Construction with K-Singular Value Decomposition. Generative Artificial Intelligence Research, 5(1). Retrieved from https://ojs.mri-pub.com/index.php/GAIR/article/view/102

Abstract

Multiple view geometry construction plays a crucial role in computer vision applications such as 3D reconstruction and multi-camera systems. The increasing demand for accurate and efficient geometric modeling highlights the necessity for advanced techniques in this field. However, existing research faces challenges in accurately estimating camera parameters and reconstructing 3D structures from multiple views due to noise and outliers in the data. In response, this paper proposes a novel approach utilizing K-Singular Value Decomposition (K-SVD) to enhance the accuracy and robustness of multiple view geometry construction. By incorporating the K-SVD technique into the traditional structure-from-motion framework, our method achieves improved performance in handling noisy datasets and outliers, consequently advancing the state-of-the-art in this area of research.

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