Title
3D2SeqViews: Aggregating Sequential Views for 3D Global Feature Learning by CNN with Hierarchical Attention Aggregation.
Abstract
Learning 3D global features by aggregating multiple views is important. Pooling is widely used to aggregate views in deep learning models. However, pooling disregards a lot of content information within views and the spatial relationship among the views, which limits the discriminability of learned features. To resolve this issue, 3D to Sequential Views (3D2SeqViews) is proposed to more effectivel...
Year
DOI
Venue
2019
10.1109/TIP.2019.2904460
IEEE Transactions on Image Processing
Keywords
Field
DocType
Three-dimensional displays,Shape,Deep learning,Solid modeling,Feature extraction,Aggregates,Convolutional neural networks
View integration,Pattern recognition,Convolutional neural network,Pooling,Spatial relationship,Artificial intelligence,Deep learning,Discriminative model,Machine learning,Feature learning,Recursion,Mathematics
Journal
Volume
Issue
ISSN
28
8
1057-7149
Citations 
PageRank 
References 
15
0.53
17
Authors
8
Name
Order
Citations
PageRank
Han Zhizhong119818.28
Honglei Lu2150.53
Zhenbao Liu336424.08
Chi-Man Vong455741.41
Yu-Shen Liu5232.05
Zwicker Matthias62513129.25
Junwei Han73501194.57
C L Philip Chen869834.35