Segmentation-based classification for 3D point clouds in the road environment

Xiang, Binbin; Yao, Jian; Lu, Xin; Li, Li; Xie, Rong; Li, Jie

doi:10.1080/01431161.2018.1455235

Cited by 23 publications

(17 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to classify images, an SVM-based edge-preservation multi-classifier relearning framework was developed to classify the high-resolution images and achieve highly accurate interpretation [19]. Xiang et al segmented the initial point clouds, and then extracted features with three popular classifiers-SVM, RF and extreme learning machine (ELM) [20]. On an average, both SVM and RF classifiers reached similar precisions and recall rates in classifying grounds, trees and buildings.…”

Section: Rule-feature-based Classification Methodsmentioning

confidence: 99%

Coarse-to-Fine Classification of Road Infrastructure Elements from Mobile Point Clouds Using Symmetric Ensemble Point Network and Euclidean Cluster Extraction

Wang

Scaioni

et al. 2019

Sensors

View full text Add to dashboard Cite

Classifying point clouds obtained from mobile laser scanning of road environments is a fundamental yet challenging problem for road asset management and unmanned vehicle navigation. Deep learning networks need no prior knowledge to classify multiple objects, but often generate a certain amount of false predictions. However, traditional clustering methods often involve leveraging a priori knowledge, but may lack generalisability compared to deep learning networks. This paper presents a classification method that coarsely classifies multiple objects of road infrastructure with a symmetric ensemble point (SEP) network and then refines the results with a Euclidean cluster extraction (ECE) algorithm. The SEP network applies a symmetric function to capture relevant structural features at different scales and select optimal sub-samples using an ensemble method. The ECE subsequently adjusts points that have been predicted incorrectly by the first step. The experimental results indicate that this method effectively extracts six types of road infrastructure elements: road surfaces, buildings, walls, traffic signs, trees and streetlights. The overall accuracy of the SEP-ECE method improves by 3.97% with respect to PointNet. The achieved average classification accuracy is approximately 99.74 % , which is suitable for practical use in transportation network management.

show abstract

Section: Rule-feature-based Classification Methodsmentioning

confidence: 99%

Coarse-to-Fine Classification of Road Infrastructure Elements from Mobile Point Clouds Using Symmetric Ensemble Point Network and Euclidean Cluster Extraction

Wang

Scaioni

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…Usages of other types of data, including point clouds, for the purpose of deep learning are being researched, but they are still underrepresented. Nevertheless, many publications are discussing deep learning on point clouds and possible network designs and corresponding approaches, for example, segmentation-based classification in the road environment, 34 treatment of LiDAR data of urban objects, 35 airborne data, 36 and others. 37 As indicated in this section, similar works mostly deal with surface inspections using 3-D sensors for industry.…”

Section: Related Workmentioning

confidence: 99%

Classification using a three-dimensional sensor in a structured industrial environment

Mikhailov

Jovančević²,

Mokhtari³

2020

J. Electron. Imag.

View full text Add to dashboard Cite

Usage of a three-dimensional (3-D) sensor and point clouds provides various benefits over the usage of a traditional camera for industrial inspection. We focus on the development of a classification solution for industrial inspection purposes using point clouds as an input. The developed approach employs deep learning to classify point clouds, acquired via a 3-D sensor, the final goal being to verify the presence of certain industrial elements in the scene. We possess the computer-aided design model of the whole mechanical assembly and an in-house developed localization module provides initial pose estimation from which 3-D point clouds of the elements are inferred. The accuracy of this approach is proved to be acceptable for industrial usage. Robustness of the classification module in relation to the accuracy of the localization algorithm is also estimated. Fig. 1 (a) Robot-based inspection system with three cameras mounted on an end effector and (b) Ensenso N35 3-D sensor.

show abstract

“…After extracting and selecting features from these segments, these segments are then classified to different categories. These methods can be classified into supervised learning methods [16,27,[43][44][45][46] and unsupervised learning methods [23,24,32,[47][48][49]. The supervised classification methods extract the overall features of the segments and train the classifier for object classification based on the extracted features.…”

Section: Point Cloud Classificationmentioning

confidence: 99%

“…Another researcher later projected the point cloud to the horizontal plane to build a feature image based on height information, and then segmentation, feature extraction and classification were applied to the feature images [43]. Recently, Binbin Xiang et al introduced a set of new features to their segments, which were fed to four classifiers and produced good classification results [46]. For unsupervised learning strategies, either thresholding or matching techniques are utilized to classify segments.…”

Section: Point Cloud Classificationmentioning

confidence: 99%

Pole-Like Street Furniture Segmentation and Classification in Mobile LiDAR Data by Integrating Multiple Shape-Descriptor Constraints

Wang

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Nowadays, mobile laser scanning is widely used for understanding urban scenes, especially for extraction and recognition of pole-like street furniture, such as lampposts, traffic lights and traffic signs. However, the start-of-art methods may generate low segmentation accuracy in the overlapping scenes, and the object classification accuracy can be highly influenced by the large discrepancy in instance number of different objects in the same scene. To address these issues, we present a complete paradigm for pole-like street furniture segmentation and classification using mobile LiDAR (light detection and ranging) point cloud. First, we propose a 3D density-based segmentation algorithm which considers two different conditions including isolated furniture and connected furniture in overlapping scenes. After that, a vertical region grow algorithm is employed for component splitting and a new shape distribution estimation method is proposed to obtain more accurate global shape descriptors. For object classification, an integrated shape constraint based on the splitting result of pole-like street furniture (SplitISC) is introduced and integrated into a retrieval procedure. Two test datasets are used to verify the performance and effectiveness of the proposed method. The experimental results demonstrate that the proposed method can achieve better classification results from both sites than the existing shape distribution method.

show abstract

Segmentation-based classification for 3D point clouds in the road environment

Cited by 23 publications

References 54 publications

Coarse-to-Fine Classification of Road Infrastructure Elements from Mobile Point Clouds Using Symmetric Ensemble Point Network and Euclidean Cluster Extraction

Coarse-to-Fine Classification of Road Infrastructure Elements from Mobile Point Clouds Using Symmetric Ensemble Point Network and Euclidean Cluster Extraction

Classification using a three-dimensional sensor in a structured industrial environment

Pole-Like Street Furniture Segmentation and Classification in Mobile LiDAR Data by Integrating Multiple Shape-Descriptor Constraints

Contact Info

Product

Resources

About