Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification

Tong, Guofeng; Li, Yong; Zhang, Weilong; Dong, Chen; Zhang, Zhenxin; Jin, Yang; Jian-jun, Zhang

doi:10.3390/rs11232846

Cited by 11 publications

(7 citation statements)

References 39 publications

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“…A few point based models such as PointRCNN [49], LaserNet [51], and PointFusion [52,53] have been designed for effective 3D-PC data formation. A Max-pooling layer based data formation scheme has been used for efficient data formulation in [54].…”

Section: Point-basedmentioning

confidence: 99%

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Mekala

Park

Dhiman³

et al. 2021

Arch Computat Methods Eng

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Autonomous Driving Vehicle (ADV) services have become a prominent motif in intelligent vehicle technology by adapting deep learning features. Automated driverless services are a hercules task due to the dynamic driving environment and the performance is deliberately reliant on the quality of data fusion from sensors. Therefore, considering advanced 3D LiDAR sensors is essential to measure the surrounding with 360 • coverage. However, accomplishing maximum autonomy is the main challenge because of debilitated and complex driving environmenst. Deep Learning (DL) based models potentially impact surrounding measures for object detection, classification, and tracking. This study describes the importance of DL-LiDAR strategies to formulate ADV research challenges followed by a comprehensive analysis of Semantic Segmentation, Data Fusion, Data Representation, Feature Extraction, Dynamic Object Detection, and Autonomous Driving-Multi-Objective Tracking mechanisms. Unlike existing review papers, we examine various deep learning influenced approaches and describe their precarious measurements. Also, we analyze the impact of systematic methods based on open-source data sets for objectx (x=classification, localization, tracking) consolidation. Most of the summaries confine scalability, and feasibility of DL techniques in ADV to accomplish an intelligent driving system without human interference. Open research problems are discussed in the last section of the article.

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Section: Point-basedmentioning

confidence: 99%

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Mekala

Park

Dhiman³

et al. 2021

Arch Computat Methods Eng

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show abstract

“…The data of Scene1, Scene2 and Scene5 can be downloaded at the author's website (http://geogother.bnu.edu.cn/teacherweb/zhangliqiang/). The point cloud of Scene3 (https://pan.baidu.com/s/1WA_YwOACBcy5jArUAmd6xA) and Scene4 (https://pan.baidu.com/s/1lOCe39sfPvkpPDTY1-TOrw) are also public datasets and are extensively used in the previously published works [4,43]. In our experiment, the proposed algorithm is implemented in MATLAB 2017b.…”

Section: (A) (B)mentioning

confidence: 99%

Multi-View Features Joint Learning with Label and Local Distribution Consistency for Point Cloud Classification

Tong

Chen

et al. 2020

Remote Sensing

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In outdoor Light Detection and Ranging (lidar)point cloud classification, finding the discriminative features for point cloud perception and scene understanding represents one of the great challenges. The features derived from defect-laden (i.e., noise, outliers, occlusions and irregularities) and raw outdoor LiDAR scans usually contain redundant and irrelevant information which adversely affects the accuracy of point semantic labeling. Moreover, point cloud features of different views have a capability to express different attributes of the same point. The simplest way of concatenating these features of different views cannot guarantee the applicability and effectiveness of the fused features. To solve these problems and achieve outdoor point cloud classification with fewer training samples, we propose a novel multi-view features and classifiers’ joint learning framework. The proposed framework uses label consistency and local distribution consistency of multi-space constraints for multi-view point cloud features extraction and classification. In the framework, the manifold learning is used to carry out subspace joint learning of multi-view features by introducing three kinds of constraints, i.e., local distribution consistency of feature space and position space, label consistency among multi-view predicted labels and ground truth, and label consistency among multi-view predicted labels. The proposed model can be well trained by fewer training points, and an iterative algorithm is used to solve the joint optimization of multi-view feature projection matrices and linear classifiers. Subsequently, the multi-view features are fused and used for point cloud classification effectively. We evaluate the proposed method on five different point cloud scenes and experimental results demonstrate that the classification performance of the proposed method is at par or outperforms the compared algorithms.

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“…uneven density, and complex shapes [5]. Scholars at home and abroad have extensively explored various point cloud data processing techniques, including point cloud denoising [6], matching [7], simplification [8], filtering [9], and classification [10], among others. The extraction of features from point clouds is a pivotal process for the analysis of shape, as well as for tasks of matching, classification, and simplification of point clouds.…”

Section: Introductionmentioning

confidence: 99%

Features extraction of point clouds based on Otsu’s algorithm

He,

Hu,

Wang

et al. 2024

Meas. Sci. Technol.

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Currently, a point cloud extraction method based on geometric features requires the configuration of two essential parameters: the neighborhood radius within the point cloud and the criterion for feature threshold selection. This article addresses the issue of manual selection of feature thresholds and proposes a feature extraction method for 3D point clouds based on the Otsu algorithm. Firstly, the curvature value of each point cloud is calculated based on the r-neighborhood of the point cloud data. Secondly, the Otsu algorithm is improved by taking the curvature values as input for the maximum inter-class variance method. The optimal segmentation threshold is obtained based on the Otsu algorithm to divide the point cloud data into two parts. Point cloud data whose curvature is greater than or equal to the threshold is extracted as feature point data. In order to verify the reliability of the algorithm presented in this paper, a method for accuracy assessment of regular point cloud data is proposed. Additionally, comparative analysis was conducted on data with varying point cloud densities and on data contaminated with Gaussian white noise using multiple methods. Experimental results show that the proposed algorithm achieves good extraction results for data with 90 percent simplification rate and low noise.

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Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification

Cited by 11 publications

References 39 publications

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Deep Learning Inspired Object Consolidation Approaches Using LiDAR Data for Autonomous Driving: A Review

Multi-View Features Joint Learning with Label and Local Distribution Consistency for Point Cloud Classification

Features extraction of point clouds based on Otsu’s algorithm

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