A Simple Methodology for 2D Reconstruction Using a CNN Model

Rodríguez-Santiago, Armando Levid; Arias-Aguilar, José Anibal; Petrilli-Barceló, Alberto Elías; Miranda-Luna, Rosebet

In recent years, Deep Learning research have demonstrated their effectiveness in digital image processing, mainly in areas with heavy computational load. Such is the case of aerial photogrammetry, where the principal objective is to generate a 2D map or a 3D model from a specific terrain. In these topics, high-efficiency in visual information processing is demanded. In this work we present a simple methodology to build an orthomosaic, our proposal is focused in replacing traditional digital imagen processing using instead a Convolutional Neuronal Network (CNN) model. The dataset of aerial images is generated from drone photographs of our university campus. The method described in this article uses a CNN model to detect matching points and RANSAC algorithm to correct feature’s correlation. Experimental results show that feature maps and matching points obtained between pair of images through a CNN are comparable with those obtained in traditional artificial vision algorithms.