scholarly journals A Network Architecture for Point Cloud Classification via Automatic Depth Images Generation

2018 ◽  
Author(s):  
Riccardo Roveri ◽  
Lukas Rahmann ◽  
A. Cengiz Oztireli ◽  
Markus Gross
Keyword(s):  
Network Architecture ◽  
Point Cloud ◽  
Depth Images ◽  
Cloud Classification ◽  
Point Cloud Classification

scholarly journals Incorporating Handcrafted Features into Deep Learning for Point Cloud Classification

2020 ◽  
Vol 12 (22) ◽  
pp. 3713
Author(s):  
Pai-Hui Hsu ◽  
Zong-Yi Zhuang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Architecture ◽  
Point Cloud ◽  
Complex Model ◽  
Cloud Data ◽  
Cloud Classification ◽  
Deep Learning Network ◽  
Application Requirements ◽  
Point Cloud Classification

Point cloud classification is an important task in point cloud data analysis. Traditional point cloud classification is conducted primarily on the basis of specific handcrafted features with a specific classifier and is often capable of producing satisfactory results. However, the extraction of crucial handcrafted features hinges on sufficient knowledge of the field and substantial experience. In contrast, while powerful deep learning algorithms possess the ability to learn features automatically, it normally requires complex network architecture and a considerable amount of calculation time to attain better accuracy of classification. In order to combine the advantages of both the methods, in this study, we integrated the handcrafted features, whose benefits were confirmed by previous studies, into a deep learning network, in the hopes of solving the problem of insufficient extraction of specific features and enabling the network to recognise other effective features through automatic learning. This was done to achieve the performance of a complex model by using a simple model and fulfil the application requirements of the remote sensing domain. As indicated by the experimental results, the integration of handcrafted features into the simple and fast-calculating PointNet model could generate a classification result that bore comparison with that generated by a complex network model such as PointNet++ or KPConv.

ERINet: Enhanced rotation-invariant network for point cloud classification

2021 ◽  
Vol 151 ◽  
pp. 180-186
Author(s):  
Ruibin Gu ◽  
Qiuxia Wu ◽  
Wing W.Y. Ng ◽  
Hongbin Xu ◽  
Zhiyong Wang
Keyword(s):  
Point Cloud ◽  
Rotation Invariant ◽  
Cloud Classification ◽  
Point Cloud Classification

DA-Net: Density-Adaptive Downsampling Network for Point Cloud Classification via End-to-End Learning

2021 ◽  
Author(s):  
Yanan Lin ◽  
Yan Huang ◽  
Shihao Zhou ◽  
Mengxi Jiang ◽  
Tianlong Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Cloud Classification ◽  
End To End ◽  
Point Cloud Classification

A weakly supervised framework for real-world point cloud classification

2021 ◽  
Author(s):  
An Deng ◽  
Yunchao Wu ◽  
Peng Zhang ◽  
Zhuheng Lu ◽  
Weiqing Li ◽  
...  
Keyword(s):  
Real World ◽  
Point Cloud ◽  
Cloud Classification ◽  
Weakly Supervised ◽  
Point Cloud Classification ◽  
World Point

Small Sample Airborne LiDAR Point Cloud Classification Based on Transfer Learning

2020 ◽  
Vol 47 (11) ◽  
pp. 1110002
Author(s):  
雷相达 Lei Xiangda ◽  
王宏涛 Wang Hongtao ◽  
赵宗泽 Zhao Zongze
Keyword(s):  
Transfer Learning ◽  
Point Cloud ◽  
Small Sample ◽  
Airborne Lidar ◽  
Cloud Classification ◽  
Point Cloud Classification
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