A Study of 3D Point Cloud Classification of Urban Structures Based on Spherical Signature Descriptor Using LiDAR Sensor Data

2019 ◽  
Vol 43 (2) ◽  
pp. 85-91 ◽  
Author(s):  
Chul Hee Bae ◽  
Sejin Lee
Keyword(s):  
Point Cloud ◽  
Sensor Data ◽  
3D Point Cloud ◽  
Urban Structures ◽  
Cloud Classification ◽  
Point Cloud Classification

Dynamic Local Geometry Capture in 3D Point Cloud Classification

2021 ◽  
Author(s):  
Shivanand Venkanna Sheshappanavar ◽  
Chandra Kambhamettu
Keyword(s):  
Point Cloud ◽  
Local Geometry ◽  
3D Point Cloud ◽  
Cloud Classification ◽  
Point Cloud Classification

Three-Dimensional Point Cloud Classification of Large Outdoor Scenes Based on Vertical Slice Sampling and Centroid Distance Histogram

2018 ◽  
Vol 45 (10) ◽  
pp. 1004001
Author(s):  
佟国峰 Tong Guofeng ◽  
杜宪策 Du Xiance ◽  
李勇 Li Yong ◽  
陈槐嵘 Chen Huairong ◽  
张庆春 Zhang Qingchun
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Slice Sampling ◽  
Cloud Classification ◽  
Centroid Distance ◽  
Outdoor Scenes ◽  
Vertical Slice ◽  
Point Cloud Classification ◽  
Distance Histogram

scholarly journals Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps

2018 ◽  
Vol 10 (8) ◽  
pp. 1192 ◽  
Author(s):  
Chen-Chieh Feng ◽  
Zhou Guo
Keyword(s):  
Land Cover ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Parameter Learning ◽  
Cloud Classification ◽  
Land Cover Map ◽  
Point Cloud Classification ◽  
Optimal Feature

The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches.

scholarly journals Dissimilarity to Class Medoids as Features for 3D Point Cloud Classification

2021 ◽  
pp. 573-581
Author(s):  
Sylvain Chabanet ◽  
Valentin Chazelle ◽  
Philippe Thomas ◽  
Hind Bril El-Haouzi
Keyword(s):  
Point Cloud ◽  
3D Point Cloud ◽  
Cloud Classification ◽  
Point Cloud Classification

scholarly journals Multi-view attention-convolution pooling network for 3D point cloud classification

2021 ◽  
Author(s):  
Wenju Wang ◽  
Tao Wang ◽  
Yu Cai
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
Information Loss ◽  
Feature Representation ◽  
3D Point Cloud ◽  
Current Output ◽  
3D Point Clouds ◽  
Cloud Classification ◽  
Feature Information ◽  
Point Cloud Classification

AbstractClassifying 3D point clouds is an important and challenging task in computer vision. Currently, classification methods using multiple views lose characteristic or detail information during the representation or processing of views. For this reason, we propose a multi-view attention-convolution pooling network framework for 3D point cloud classification tasks. This framework uses Res2Net to extract the features from multiple 2D views. Our attention-convolution pooling method finds more useful information in the input data related to the current output, effectively solving the problem of feature information loss caused by feature representation and the detail information loss during dimensionality reduction. Finally, we obtain the probability distribution of the model to be classified using a full connection layer and the softmax function. The experimental results show that our framework achieves higher classification accuracy and better performance than other contemporary methods using the ModelNet40 dataset.

scholarly journals Classification of Power Facility Point Clouds from Unmanned Aerial Vehicles Based on Adaboost and Topological Constraints

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4717 ◽  
Author(s):  
Yuxuan Liu ◽  
Mitko Aleksandrov ◽  
Sisi Zlatanova ◽  
Junjun Zhang ◽  
Fan Mo ◽  
...  
Keyword(s):  
Transmission Lines ◽  
Point Cloud ◽  
Power Transmission ◽  
Machine Learning Algorithms ◽  
Power Transmission Lines ◽  
Topological Constraints ◽  
Adaboost Algorithm ◽  
Cloud Classification ◽  
Point Cloud Classification

Machine learning algorithms can be well suited to LiDAR point cloud classification, but when they are applied to the point cloud classification of power facilities, many problems such as a large number of computational features and low computational efficiency can be encountered. To solve these problems, this paper proposes the use of the Adaboost algorithm and different topological constraints. For different objects, the top five features with the best discrimination are selected and combined into a strong classifier by the Adaboost algorithm, where coarse classification is performed. For power transmission lines, the optimum scales are selected automatically, and the coarse classification results are refined. For power towers, it is difficult to distinguish the tower from vegetation points by only using spatial features due to the similarity of their proposed key features. Therefore, the topological relationship between the power line and power tower is introduced to distinguish the power tower from vegetation points. The experimental results show that the classification of power transmission lines and power towers by our method can achieve the accuracy of manual classification results and even be more efficient.

scholarly journals An Effective Encoding Method Based on Local Information for 3D Point Cloud Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39369-39377
Author(s):  
Yanan Song ◽  
Liang Gao ◽  
Xinyu Li ◽  
Quan-Ke Pan
Keyword(s):  
Point Cloud ◽  
Local Information ◽  
3D Point Cloud ◽  
Cloud Classification ◽  
Encoding Method ◽  
Point Cloud Classification
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