Image-based 3d object reconstruction: State-of-the-art and trends in the deep learning era

Xian Feng Han, Hamid Laga, Mohammed Bennamoun

Research output: Contribution to journalArticlepeer-review

98 Citations (Web of Science)

Abstract

3D reconstruction is a longstanding ill-posed problem, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. Since 2015, image-based 3D reconstruction using convolutional neural networks (CNN) has attracted increasing interest and demonstrated an impressive performance. Given this new era of rapid evolution, this article provides a comprehensive survey of the recent developments in this field. We focus on the works which use deep learning techniques to estimate the 3D shape of generic objects either from a single or multiple RGB images. We organize the literature based on the shape representations, the network architectures, and the training mechanisms they use. While this survey is intended for methods which reconstruct generic objects, we also review some of the recent works which focus on specific object classes such as human body shapes and faces. We provide an analysis and comparison of the performance of some key papers, summarize some of the open problems in this field, and discuss promising directions for future research.

Original languageEnglish
Article number8908779
Pages (from-to)1578-1604
Number of pages27
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume43
Issue number5
DOIs
Publication statusPublished - 1 May 2021

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