Next Article in Journal
Applications of Nanofluids for the Thermal Enhancement in Radiative and Dissipative Flow over a Wedge
Next Article in Special Issue
Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
Previous Article in Journal
Performance Evaluation of Pilot-scale Hybrid Anaerobic Baffled Reactor (HABR) to Process Dyeing Wastewater Based on Grey Relational Analysis
Previous Article in Special Issue
Deep Forest-Based Monocular Visual Sign Language Recognition
Article Menu
Issue 10 (May-2) cover image

Export Article

Open AccessArticle

3D Wireframe Modeling and Viewpoint Estimation for Multi-Class Objects Combining Deep Neural Network and Deformable Model Matching

College of Electronic Science, National University of Defense Technology; Changsha HN 731, China
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(10), 1975;
Received: 25 February 2019 / Revised: 29 April 2019 / Accepted: 8 May 2019 / Published: 14 May 2019
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology)
PDF [5916 KB, uploaded 27 May 2019]


The accuracy of 3D viewpoint and shape estimation from 2D images has been greatly improved by machine learning, especially deep learning technology such as the convolution neural network (CNN). However, current methods are always valid only for one specific category and have exhibited poor performance when generalized to other categories, which means that multiple detectors or networks are needed for multi-class object image cases. In this paper, we propose a method with strong generalization ability, which incorporates only one CNN with deformable model matching processing for the 3D viewpoint and the shape estimation of multi-class object image cases. The CNN is utilized to detect keypoints of the potential object from the image, while a deformable model matching stage is designed to conduct 3D wireframe modeling and viewpoint estimation simultaneously with the support of the detected keypoints. Besides, parameter estimation by deformable model matching processing has robust fault-tolerance to the keypoint detection results containing mistaken keypoints. The proposed method is evaluated on Pascal3D+ dataset. Experiments show that the proposed method performs well in both parameter estimation accuracy and the multi-class objects generalization. This research is a useful exploration to extend the generalization of deep learning in specific tasks. View Full-Text
Keywords: 3D vision; viewpoint estimation; wireframe modeling; deformable model 3D vision; viewpoint estimation; wireframe modeling; deformable model

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Ren, X.; Jiang, L.; Tang, X.; Liu, W. 3D Wireframe Modeling and Viewpoint Estimation for Multi-Class Objects Combining Deep Neural Network and Deformable Model Matching. Appl. Sci. 2019, 9, 1975.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top