3D360: Automated Construction of Navigable 3D Models from Surrounding Real Environments

Abstract

In this research paper, a system capable of taking as input multiple 2-dimensional images of the surrounding environment from a particular position and creating a 3-dimensional model from them, with navigation possible inside it, for the 360 degree view is developed. Existing approaches for image stitching, which use SIFT features, along with approaches for depth estimation, which use supervised learning to train a Markov Random Field (MRF), are modified in this paper in order to improve their efficiency. Also, an improvement in accuracy of depth estimation is suggested for the 3-dimensional model using matching SIFT features from the multiple input images. A method for enabling navigation in the 3D model through which we prevent motion in areas where movement is not possible is outlined, thus making the 3-dimensional model realistic and suitable for practical use. The proposed system is also an application of Neural Networks.