A Distance Correlation Approach for Optimum Multiscale Selection in 3D Point Cloud Classification

Supervised classification of 3D point clouds using machine learning algorithms and handcrafted local features as covariates frequently depends on the size of the neighborhood (scale) around each point used to determine those features. It is therefore crucial to estimate the scale or scales providing the best classification results. In this work, we propose three methods to estimate said scales, all of them based on calculating the maximum values of the distance correlation (DC) functions between the features and the label assigned to each point. The performance of the methods was tested using simulated data, and the method presenting the best results was applied to a benchmark data set for point cloud classification. This method consists of detecting the local maximums of DC functions previously smoothed to avoid choosing scales that are very close to each other. Five different classifiers were used: linear discriminant analysis, support vector machines, random forest, multinomial logistic regression and multilayer perceptron neural network. The results obtained were compared with those from other strategies available in the literature, being favorable to our approach.

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A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification

Applied Sciences ◽

10.3390/app9050951 ◽

2019 ◽

Vol 9 (5) ◽

pp. 951 ◽

Cited By ~ 6

Author(s):

Yong Li ◽

Guofeng Tong ◽

Xiance Du ◽

Xiang Yang ◽

Jianjun Zhang ◽

...

Keyword(s):

Point Cloud ◽

Single Point ◽

Point Clouds ◽

Optimization Method ◽

Gaussian Kernel ◽

Support Vector ◽

Angle Distribution ◽

Features Fusion ◽

Cloud Classification ◽

Point Cloud Classification

3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification algorithm based on a single point multilevel features fusion and pyramid neighborhood optimization are proposed for a Airborne Laser Scanning (ALS) point cloud. First, the proposed algorithm determines the neighborhood region of each point, after which the features of each single point are extracted. For the characteristics of the ALS point cloud, two new feature descriptors are proposed, i.e., a normal angle distribution histogram and latitude sampling histogram. Following this, multilevel features of a single point are constructed by multi-resolution of the point cloud and multi-neighborhood spaces. Next, the features are trained by the Support Vector Machine based on a Gaussian kernel function, and the points are classified by the trained model. Finally, a classification results optimization method based on a multi-scale pyramid neighborhood constructed by a multi-resolution point cloud is used. In the experiment, the algorithm is tested by a public dataset. The experimental results show that the proposed algorithm can effectively classify large-scale ALS point clouds. Compared with the existing algorithms, the proposed algorithm has a better classification performance.

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

Applied Intelligence ◽

10.1007/s10489-021-02840-2 ◽

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.

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

Sensors ◽

10.3390/s19214717 ◽

2019 ◽

Vol 19 (21) ◽

pp. 4717 ◽

Cited By ~ 2

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.

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ALS Point Cloud Classification With Small Training Data Set Based on Transfer Learning

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2019.2947608 ◽

2020 ◽

Vol 17 (8) ◽

pp. 1406-1410 ◽

Cited By ~ 1

Author(s):

Chuan Zhao ◽

Haitao Guo ◽

Jun Lu ◽

Donghang Yu ◽

Daoji Li ◽

...

Keyword(s):

Transfer Learning ◽

Point Cloud ◽

Training Data ◽

Data Set ◽

Cloud Classification ◽

Point Cloud Classification

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Optimum Scale Selection for 3D Point Cloud Classification through Distance Correlation

Functional and High-Dimensional Statistics and Related Fields - Contributions to Statistics ◽

10.1007/978-3-030-47756-1_28 ◽

2020 ◽

pp. 213-220

Author(s):

Manuel Oviedo de la Fuente ◽

Carlos Cabo ◽

Celestino Ordóñez ◽

Javier Roca-Pardiñas

Keyword(s):

Point Cloud ◽

3D Point Cloud ◽

Scale Selection ◽

Distance Correlation ◽

Cloud Classification ◽

Optimum Scale ◽

Selection For ◽

Point Cloud Classification

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SEMANTIC3D.NET: A NEW LARGE-SCALE POINT CLOUD CLASSIFICATION BENCHMARK

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-w1-91-2017 ◽

2017 ◽

Vol IV-1/W1 ◽

pp. 91-98 ◽

Cited By ~ 88

Author(s):

T. Hackel ◽

N. Savinov ◽

L. Ladicky ◽

J. D. Wegner ◽

K. Schindler ◽

...

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Point Clouds ◽

Full Potential ◽

3D Point Cloud ◽

Learning Methods ◽

Data Set ◽

Cloud Classification ◽

Wide Range ◽

Point Cloud Classification

This paper presents a new 3D point cloud classification benchmark data set with over four billion manually labelled points, meant as input for data-hungry (deep) learning methods. We also discuss first submissions to the benchmark that use deep convolutional neural networks (CNNs) as a work horse, which already show remarkable performance improvements over state-of-the-art. CNNs have become the de-facto standard for many tasks in computer vision and machine learning like semantic segmentation or object detection in images, but have no yet led to a true breakthrough for 3D point cloud labelling tasks due to lack of training data. With the massive data set presented in this paper, we aim at closing this data gap to help unleash the full potential of deep learning methods for 3D labelling tasks. Our semantic3D.net data set consists of dense point clouds acquired with static terrestrial laser scanners. It contains 8 semantic classes and covers a wide range of urban outdoor scenes: churches, streets, railroad tracks, squares, villages, soccer fields and castles. We describe our labelling interface and show that our data set provides more dense and complete point clouds with much higher overall number of labelled points compared to those already available to the research community. We further provide baseline method descriptions and comparison between methods submitted to our online system. We hope semantic3D.net will pave the way for deep learning methods in 3D point cloud labelling to learn richer, more general 3D representations, and first submissions after only a few months indicate that this might indeed be the case.

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A CURVATURE BASED ADAPTIVE NEIGHBORHOOD FOR INDIVIDUAL POINT CLOUD CLASSIFICATION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w7-219-2017 ◽

2017 ◽

Vol XLII-2/W7 ◽

pp. 219-225 ◽

Cited By ~ 2

Author(s):

E. He ◽

Q. Chen ◽

H. Wang ◽

X. Liu

Keyword(s):

Point Cloud ◽

Machine Learning Algorithms ◽

The Past ◽

Cloud Classification ◽

Individual Point ◽

3D Scene ◽

Adaptive Neighborhood ◽

Point Cloud Classification ◽

Neighborhood Method ◽

Analysis Point

As a key step in 3D scene analysis, point cloud classification has gained a great deal of concerns in the past few years. Due to the uneven density, noise and data missing in point cloud, how to automatically classify the point cloud with a high precision is a very challenging task. The point cloud classification process typically includes the extraction of neighborhood based statistical information and machine learning algorithms. However, the robustness of neighborhood is limited to the density and curvature of the point cloud which lead to a label noise behavior in classification results. In this paper, we proposed a curvature based adaptive neighborhood for individual point cloud classification. Our main improvement is the curvature based adaptive neighborhood method, which could derive ideal 3D point local neighborhood and enhance the separability of features. The experiment result on Oakland benchmark dataset shows that the proposed method can effectively improve the classification accuracy of point cloud.

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