Advances in Intelligent Systems and Computing
Image reconstruction is primarily an inverse problem, which directs the attention to reconstructing the original image from the given 2D image which may be blurred, consisting of some noise, and/or some damaged regions. The conversion to 3D involves finding out missing data. Rotation in conjunction with visualization from numerous perspectives of the image is facilitated by the third dimension for 3D reconstruction. Detailed and swift 3D reconstruction has established its applications in various fields such as Computer Vision, Medical Imaging, Virtual Reality, etc. The purpose of this paper is to bring attention to the thorough study of some state-of-the-art methods, which is used for fast 3D reconstruction. These methods include techniques, namely “Patch based Multi-View Stereo (PMVS), DAISY Descriptors, 3D point cloud, 3D Template matching, Recurrent Neural Network”. In the end, a comparative study of these methods based on various parameters is done to guide the readers to check the suitability of each approach.