CONVERTING IMAGES TO 3D MODELS USING PHOTOGRAMMETRY

Abstract

Photogrammetry, a technique that enables the creation of 3D models from 2D images, holds great potential for various applications. However, the current manual and time- consuming nature of the process hinders its widespread adoption. This research aims to address these challenges by developing a faster and more efficient photogrammetry workflow. Traditionally, photogrammetry relied on stereo pairs of images to generate digital elevation models. In this study, we propose a novel approach where multiple images of an object captured from various angles are used, with the prerequisite that the background scene remains static. These images are then loaded into our advanced photogrammetry software. To expedite the 3D modelling process, our methodology leverages mathematical algorithms and Al-driven learning-based techniques. By combining the power of computational mathematics and artificial intelligence, we aim to automate and optimize the reconstruction process, reducing the need for extensive manual intervention. The resulting output will be a 3D model with lower density but still possessing a high level of accuracy.