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Image-Based 3D Object Reconstruction : State-of-the-Art and Trends in the Deep Learning Era

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.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.43 No.5 May 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1578 - 1604

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

BENTUK GALAKSI
3D VIDEO
3D RECONSTRUCTION
DEPTH ESTIMATION
SLAM
CNN
SFM
LSTM
3D FACE
3D HUMAN BODY

Info Detail Spesifik

DOI: 10.1109/TPAMI.2019.2954885

Pernyataan Tanggungjawab

Xian-Feng Han, Hamid Laga, Mohammed Bennamoun

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