A review on 3D image reconstruction on specific and generic objects. (2023)

• Record Type: Journal Article
• Authors: Shalma, H.; Selvaraj, P.
• Is Part Of: Materials today. Volume 80:Part 3(2023)
• Page Start: 2400
• Page End: 2405
• Publication Date: 2023
• Subjects: Reconstruction -- Computer vision -- Segmentation -- Object recognition -- 3D deep learning -- Shape representations; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.06.371
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: With the emergence of various techniques involved in deep learning the researchers of computer vision tends to focus on the strategies such as object recognition and segmentation of image. This has inclined to accomplish the deep learning techniques in 3D reconstruction of both specific and generic objects. As the space for reconstruction of 3D images either in single or multi view has envisioned the researchers to concentrate on the available technologies used for reconstruction. With the available built in methods and technologies in deep learning, the performance of the proposed methods were reviewed and analyzed using several parameters. As the remaking of 2D images is still in the beginning stage, it is important to study the 3D shape representations, various network architecture, methodologies and approaches behind 3D reconstruction. In this work a review of deep learning methods for single or multiple RGB images of specific and generic object 3D reconstruction was done. Several methods and their importance were also discussed along with the challenges encountered and with further research directions. This paper critically analyzes the various 3D Shaped Representations, 3D Data Network Architectures, Depth Estimation methods, Multi View Representations and the Data Representation Techniques.