Abstract: The conversion of 2D images to 3D models has become a significant area of research in computer vision and graphics. This project explores the development of a web-based system that leverages the Flask framework for backend processing to convert 2D images into 3D representations. The core objective of the project is to implement a simple yet effective pipeline that processes input 2D images and generates a 3D model through various computer vision algorithms and machine learning techniques. Using Flask, a lightweight web framework for Python, the system receives 2D images from users, processes them through pre-trained models or algorithms, and then outputs a 3D model or visualization. The 3D model is constructed by inferring depth, texture, and geometric properties from the 2D image. This model can be further visualized in the browser using WebGL or exported into standard formats like STL or OBJ for use in 3D printing or digital modeling applications. The project aims to demonstrate the potential of combining web technologies with advanced image processing techniques to create accessible tools for 3D model generation from basic 2D inputs. This could be applied in various fields, including digital design, augmented reality, and game development, offering a convenient and scalable solution for converting 2D images into 3D assets.
Keywords: 2D to 3D conversion, computer vision, 3D reconstruction, Flask framework, machine learning, image processing, WebGL visualization, depth estimation, STL/OBJ export, web-based system, digital modeling, 3D printing, augmented reality, game development, geometric inference.
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DOI:
10.17148/IJARCCE.2025.14554
