Paper Info
Title
PGNet: Progressive Feature Guide Learning Network for Three-dimensional Shape Recognition
Abstract
AbstractThree-dimensional (3D) shape recognition is a popular topic and has potential application value in the field of computer vision. With the recent proliferation of deep learning, various deep learning models have achieved state-of-the-art performance. Among them, multiview-based 3D shape representation has received increased attention in recent years, and related approaches have shown significant improvement in 3D shape recognition. However, these methods focus on feature learning based on the design of the network and ignore the correlation among views. In this article, we propose a novel progressive feature guide learning network (PGNet) that focuses on the correlation among multiple views and integrates multiple modalities for 3D shape recognition. In particular, we propose two information fusion schemes from visual and feature aspects. The visual fusion scheme focuses on the view level and employs the soft-attention model to define the weights of views for visual information fusion. The feature fusion scheme focuses on the feature dimension information and employs the quantified feature as the mask to further optimize the feature. These two schemes jointly construct a PGNet for 3D shape representation. The classic ModelNet40 and ShapeNetCore55 datasets are applied to demonstrate the performance of our approach. The corresponding experiment also demonstrates the superiority of our approach.
Year
DOI
Venue
2021
10.1145/3443708
ACM Transactions on Multimedia Computing, Communications, and Applications
Keywords
DocType
Volume
3D model retrieval, multi-modal, 3D shape recognition
Journal
17
Issue
ISSN
Citations 
3
1551-6857
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Nie Jie15112.88
Zhiqiang Wei230735.82
Weizhi Nie357742.74
Anan Liu482362.46