3D Large-Scale Point Cloud Semantic Segmentation Using Optimal Feature Description Vector Network: OFDV-Net

Li, Jian; Sun, Quan; Chen, Keru; Cui, Hao; Huangfu, Kuan; Chen, Xiaolong

doi:10.1109/access.2020.3044166

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Point cloud feature identification refers to the process of matching unknown features with known ones to classify features. In recent years, there has been a growing number of automatic classification methods that use deep learning for spatiotemporal data, such as remote sensing images, laser point clouds, SAR, and others [29][30][31][32][33][34]. These methods are mainly based on the supervised classification of statistical learning data, which requires learning sample data in advance to determine model parameters and then using the obtained model to classify sub-data.…”

Section: Identification Methods For Point Cloud Featuresmentioning

confidence: 99%

“…et al proposed the PointNet method for deep learning on point sets for 3D classification and segmentation [40], while Luis A. Alexandre performed a comparative evaluation on 3D point clouds, exploring both object and category recognition performance and describing existing feature extraction algorithms in a publicly available point cloud library [41]. Li, J. et al applied the OFDV Net to standard public exterior large-scale point cloud dataset segmentation [33] and achieved good extraction effects. Pritpal Singh et al provided a quantum-clustering optimization method for COVID-19 CT scan image segmentation [42] and a type-2 neutrosophic-entropy-fusion-based multiple thresholding method for brain tumor tissue structure segmentation [43].…”

Section: Identification Methods For Point Cloud Featuresmentioning

confidence: 99%

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A 3D Point Cloud Feature Identification Method Based on Improved Point Feature Histogram Descriptor

Wang,

Xiong,

Zhang

et al. 2023

Electronics

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A significant amount of research has been conducted on the segmentation of large-scale 3D point clouds. However, efficient point cloud feature identification from segmentation results is an essential capability for computer vision and surveying tasks. Feature description methods are algorithms that convert the point set of the point cloud feature into vectors or matrices that can be used for identification. While the point feature histogram (PFH) is an efficient descriptor method, it does not work well with objects that have smooth surfaces, such as planar, spherical, or cylindrical objects. This paper proposes a 3D point cloud feature identification method based on an improved PFH descriptor with a feature-level normal that can efficiently distinguish objects with smooth surfaces. Firstly, a feature-level normal is established, and then the relationship between each point’s normal and feature-level normal is calculated. Finally, the unknown feature is identified by comparing the similarity of the type-labeled feature and the unknown feature. The proposed method obtains an overall identification accuracy ranging from 71.9% to 81.9% for the identification of street lamps, trees, and buildings.

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Section: Identification Methods For Point Cloud Featuresmentioning

confidence: 99%

Section: Identification Methods For Point Cloud Featuresmentioning

confidence: 99%

A 3D Point Cloud Feature Identification Method Based on Improved Point Feature Histogram Descriptor

Wang,

Xiong,

Zhang

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…The VPO-based Global Motion Prediction proposed in this paper classifies HPO and VPO in an input point cloud, and searches for the global motion in the VPO. A conventional classification of a point cloud [24]- [27] requires a higher computational complexity than a two-dimensional image because it is necessary to analyze the distribution of huge point data in a three-dimensional space. However, since the LiDARbased sparse point cloud is utilized in low-power devices such as vehicles, point cloud classification technology with low computational complexity is required.…”

Section: A Histogram-based Point Cloud Classificationmentioning

confidence: 99%

LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction

et al. 2022

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“…There were some applications that used semantic segmentation models for unique environments for robot navigation. A study [24] proposed a new model for performing semantic segmentation on 3D point clouds. Although their model performs with high accuracy on a platform with workstationlevel computing power, 3D point cloud data is often very expensive to both collect and compute with, and such data types may not be best suited for low-power mobile platforms, where computing power is extremely limited.…”

Section: B Semantic Segmentation-based Object Detectionmentioning

confidence: 99%

Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones

Zelek

Jeon

2022

IEEE Access

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The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of detecting irregularly shaped obstacles such as road cracks, debris on the streets, and water puddles. In this paper, we characterize a number of state-of-the-art Fully Convolutional Network (FCN) models on mobile embedded platforms for self-navigation at these sites containing extremely irregular obstacles. We evaluate the models in terms of accuracy, performance, and energy efficiency. We present a few optimizations for our designed vision system. Lastly, we discuss the trade-offs of these models for a couple of mobile platforms that can each perform self-navigation. To enable vehicles to safely navigate earthquake-struck zones, we compile a new annotated image database of various earthquake impacted regions that is different than traditional road damage databases. We train our database with a number of state-of-the-art semantic segmentation models in order to identify obstacles unique to earthquake-struck zones. Based on the statistics and tradeoffs, an optimal FCN model is selected and applied to the mobile vehicular platforms. To our best knowledge, this is the first study that identifies unique challenges and discusses the accuracy, performance, and energy impact of edge-based self-navigation mobile vehicles for earthquake-struck zones. Our proposed database and trained models are publicly available.INDEX TERMS Convolutional Neural Networks, Edge Computing, Self Driving, Semantic Segmentation I. INTRODUCTIONWith global-wide monitoring and technology evolution, the riskiness of natural disaster is getting lower. However, there are still deadly incidents such as earthquakes. Earthquakes are a geologic inevitability of some countries and states. To reduce the effects from earthquakes, we should think about the ways to quickly search earthquake-struck zones and safely rescue more lives. For this purpose, unmanned autonomous vehicles will be effective, which navigate impacted sites while localizing the people and reporting the damages without risking the lives of others, such as firefighters and other rescue workers. In the natural disaster impacted sites, edge-based small automobiles would be more cost effective and feasible solutions because these small cars can navigate underneath collapsed buildings, unstable bridges, and narrow walkways that are difficult to be driven in by a full-sized vehicle, and also hidden from the viewing angles of aerial solutions such as drones. Though the self-navigation research and industry has been quickly evolving, most of the solutions focus on detecting fairly regular-shaped objects such as hu-mans, buildings, and streets from a full-sized vehicle's perspective. However, these solutions cannot be directly applied to earthquake-struck zones because of the unique obstacles such as debris, cracks, and puddles on the streets, which the mobile edge vehicle perceive...

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3D Large-Scale Point Cloud Semantic Segmentation Using Optimal Feature Description Vector Network: OFDV-Net

Cited by 6 publications

References 20 publications

A 3D Point Cloud Feature Identification Method Based on Improved Point Feature Histogram Descriptor

A 3D Point Cloud Feature Identification Method Based on Improved Point Feature Histogram Descriptor

LiDAR Point Cloud Compression by Vertically Placed Objects Based on Global Motion Prediction

Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones

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