QuantumVision: A Hybrid Quantum–Classical Framework for 3D Image Reconstruction from Partial Views

This work presents QuantumVision, a conceptual hybrid quantum–classical framework for three-dimensional (3D) image reconstruction from partial two-dimensional views. Recent deep learning approaches such as Large Reconstruction Models (LRM) and diffusion-based 3D methods achieve high reconstruction quality but suffer from extreme computational costs and scalability limitations. QuantumVision explores the integration of Quantum Image Processing (QIP) techniques as preprocessing and optimization aids within classical 3D reconstruction pipelines. The framework analyzes computational bottlenecks in modern reconstruction systems and identifies stages where quantum-assisted methods—such as quantum edge detection, amplitude-based feature encoding, and variational quantum circuits—could theoretically improve efficiency and robustness. This paper does not claim experimental quantum advantage; instead, it provides a structured and realistic conceptual foundation for future research on quantum-enhanced 3D reconstruction. The work is intended for researchers and students interested in computer vision, hybrid quantum–classical machine learning, and emerging applications of quantum computing in visual perception.