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.

scholarly journals Classification of Point Clouds for Indoor Components Using Few Labeled Samples

2020 ◽  
Vol 12 (14) ◽  
pp. 2181
Author(s):  
Hangbin Wu ◽  
Huimin Yang ◽  
Shengyu Huang ◽  
Doudou Zeng ◽  
Chun Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Neighborhood Search ◽  
Learning Methods ◽  
Semantic Classification ◽  
Cloud Classification ◽  
Mixed Features ◽  
Indoor Scenarios ◽  
Point Cloud Classification

The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89–19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios.

scholarly journals Point Cloud Classification Algorithm Based on the Fusion of the Local Binary Pattern Features and Structural Features of Voxels

2021 ◽  
Vol 13 (16) ◽  
pp. 3156
Author(s):  
Yong Li ◽  
Yinzheng Luo ◽  
Xia Gu ◽  
Dong Chen ◽  
Fang Gao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Local Binary Pattern ◽  
Random Forest Classifier ◽  
Normal Vector ◽  
Point Cloud Data ◽  
Color Information ◽  
Cloud Data ◽  
Cloud Classification ◽  
Local Difference ◽  
Point Cloud Classification

Point cloud classification is a key technology for point cloud applications and point cloud feature extraction is a key step towards achieving point cloud classification. Although there are many point cloud feature extraction and classification methods, and the acquisition of colored point cloud data has become easier in recent years, most point cloud processing algorithms do not consider the color information associated with the point cloud or do not make full use of the color information. Therefore, we propose a voxel-based local feature descriptor according to the voxel-based local binary pattern (VLBP) and fuses point cloud RGB information and geometric structure features using a random forest classifier to build a color point cloud classification algorithm. The proposed algorithm voxelizes the point cloud; divides the neighborhood of the center point into cubes (i.e., multiple adjacent sub-voxels); compares the gray information of the voxel center and adjacent sub-voxels; performs voxel global thresholding to convert it into a binary code; and uses a local difference sign–magnitude transform (LDSMT) to decompose the local difference of an entire voxel into two complementary components of sign and magnitude. Then, the VLBP feature of each point is extracted. To obtain more structural information about the point cloud, the proposed method extracts the normal vector of each point and the corresponding fast point feature histogram (FPFH) based on the normal vector. Finally, the geometric mechanism features (normal vector and FPFH) and color features (RGB and VLBP features) of the point cloud are fused, and a random forest classifier is used to classify the color laser point cloud. The experimental results show that the proposed algorithm can achieve effective point cloud classification for point cloud data from different indoor and outdoor scenes, and the proposed VLBP features can improve the accuracy of point cloud classification.

scholarly journals Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 323 ◽  
Author(s):  
Gordana Jakovljevic ◽  
Miro Govedarica ◽  
Flor Alvarez-Taboada ◽  
Vladimir Pajic
Keyword(s):  
Deep Learning ◽  
Flood Risk ◽  
Point Cloud ◽  
Accuracy Assessment ◽  
Point Clouds ◽  
Water Levels ◽  
Cloud Classification ◽  
Ground Point ◽  
Flood Risk Mapping ◽  
Point Cloud Classification

Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and –0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas.

scholarly journals Orientation-Encoding CNN for Point Cloud Classification and Segmentation

2021 ◽  
Vol 3 (3) ◽  
pp. 601-614
Author(s):  
Hongbin Lin ◽  
Wu Zheng ◽  
Xiuping Peng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Feature Learning ◽  
Point Clouds ◽  
Point Sets ◽  
Learning Network ◽  
Rule Structure ◽  
Visual Tasks ◽  
Deep Learning Network ◽  
Point Cloud Classification

With the introduction of effective and general deep learning network frameworks, deep learning based methods have achieved remarkable success in various visual tasks. However, there are still tough challenges in applying them to convolutional neural networks due to the lack of a potential rule structure of point clouds. Therefore, by taking the original point clouds as the input data, this paper proposes an orientation-encoding (OE) convolutional module and designs a convolutional neural network for effectively extracting local geometric features of point sets. By searching for the same number of points in 8 directions and arranging them in order in 8 directions, the OE convolution is then carried out according to the number of points in the direction, which realizes the effective feature learning of the local structure of the point sets. Further experiments on diverse datasets show that the proposed method has competitive performance on classification and segmentation tasks of point sets.

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

2017 ◽  
Vol IV-1/W1 ◽  
pp. 91-98 ◽  
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.

A study on the classification of vegetation point cloud based on the straw checkerbo-ard barriers area of random forest

2021 ◽  
pp. 1-13
Author(s):  
Tiebo Sun ◽  
Jinhao Liu ◽  
Jiangming Kan ◽  
Tingting Sui
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Random Forest Algorithm ◽  
Cloud Data ◽  
Cloud Classification ◽  
Point Cloud Classification

Aiming at the problem of automatic classification of point cloud in the investigation of vegetation resources in the straw checkerboard barriers region, an improved random forest point cloud classification algorithm was proposed. According to the problems of decision tree redundancy and absolute majority voting in the existing random forest algorithm, first the similarity of the decision tree was calculated based on the tree edit distance, further clustered reduction based on the maximum and minimum distance algorithm, and then introduced classification accuracy of decision tree to construct weight matrix to implement weighted voting at the voting stage. Before random forest classification, based on the characteristics of point cloud data, a total of 20 point cloud single-point features and multi-point statistical features were selected to participate in point cloud classification, based on the point cloud data spatial distribution characteristics, three different scales for selecting point cloud neighborhoods were set based on the point cloud density, point cloud classification feature sets at different scales were constructed, optimizing important features of point cloud to participate in point cloud classification calculation after variable importance scored. The experimental results showed that the point cloud classification based on the optimized random forest algorithm in this paper achieved a total classification accuracy of 94.15% in dataset 1 acquired by lidar, the overall accuracy of classification on dataset 2 obtained by dense matching reaches 92.03%, both were higher than the unoptimized random forest algorithm and MRF, SVM point cloud classification method, and dimensionality reduction through feature optimization can greatly improve the efficiency of the algorithm.

scholarly journals PEMCNet: An Efficient Multi-Scale Point Feature Fusion Network for 3D LiDAR Point Cloud Classification

2021 ◽  
Vol 13 (21) ◽  
pp. 4312
Author(s):  
Genping Zhao ◽  
Weiguang Zhang ◽  
Yeping Peng ◽  
Heng Wu ◽  
Zhuowei Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Feature Learning ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Multi Scale ◽  
Cloud Classification ◽  
Public Datasets ◽  
Point Cloud Classification ◽  
3D Lidar ◽  
Point Feature

Point cloud classification plays a significant role in Light Detection and Ranging (LiDAR) applications. However, most available multi-scale feature learning networks for large-scale 3D LiDAR point cloud classification tasks are time-consuming. In this paper, an efficient deep neural architecture denoted as Point Expanded Multi-scale Convolutional Network (PEMCNet) is developed to accurately classify the 3D LiDAR point cloud. Different from traditional networks for point cloud processing, PEMCNet includes successive Point Expanded Grouping (PEG) units and Absolute and Relative Spatial Embedding (ARSE) units for representative point feature learning. The PEG unit enables us to progressively increase the receptive field for each observed point and aggregate the feature of a point cloud at different scales but without increasing computation. The ARSE unit following the PEG unit furthermore realizes representative encoding of points relationship, which effectively preserves the geometric details between points. We evaluate our method on both public datasets (the Urban Semantic 3D (US3D) dataset and Semantic3D benchmark dataset) and our new collected Unmanned Aerial Vehicle (UAV) based LiDAR point cloud data of the campus of Guangdong University of Technology. In comparison with four available state-of-the-art methods, our methods ranked first place regarding both efficiency and accuracy. It was observed on the public datasets that with a 2% increase in classification accuracy, over 26% improvement of efficiency was achieved at the same time compared to the second efficient method. Its potential value is also tested on the newly collected point cloud data with over 91% of classification accuracy and 154 ms of processing time.

Deep Learning Model for Point Cloud Classification Based on Graph Convolutional Network

2019 ◽  
Vol 56 (21) ◽  
pp. 211004
Author(s):  
王旭娇 Wang Xujiao ◽  
马杰 Ma Jie ◽  
王楠楠 Wang Nannan ◽  
马鹏飞 Ma Pengfei ◽  
杨立闯 Yang Lichaung
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Learning Model ◽  
Convolutional Network ◽  
Cloud Classification ◽  
Point Cloud Classification ◽  
Deep Learning Model
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