GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification

Long, Hoang Thanh; Lee, Suk-Hwan; Lee, Eung Joo; Kwon, Ki-Ryong

doi:10.3390/app12010483

Cited by 13 publications

(4 citation statements)

References 50 publications

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“…Then, the accumulator count of grids of the rasterized Hough space was computed and used as an input for a CNN model for object classification. Hoang et al [23] proposed a Multi-Scale Attentive Aggregation Network (MSAAN) to acquire the consistency of the descriptors. Aijazi et al [24] developed a novel framework, GSV-NET, for extracting and combining both global and regional descriptors using a 3D wide-inception CNN structure.…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

D-Net: A Density-Based Convolutional Neural Network for Mobile LiDAR Point Clouds Classification in Urban Areas

et al. 2023

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The 3D semantic segmentation of a LiDAR point cloud is essential for various complex infrastructure analyses such as roadway monitoring, digital twin, or even smart city development. Different geometric and radiometric descriptors or diverse combinations of point descriptors can extract objects from LiDAR data through classification. However, the irregular structure of the point cloud is a typical descriptor learning problem—how to consider each point and its surroundings in an appropriate structure for descriptor extraction? In recent years, convolutional neural networks (CNNs) have received much attention for automatic segmentation and classification. Previous studies demonstrated deep learning models’ high potential and robust performance for classifying complicated point clouds and permutation invariance. Nevertheless, such algorithms still extract descriptors from independent points without investigating the deep descriptor relationship between the center point and its neighbors. This paper proposes a robust and efficient CNN-based framework named D-Net for automatically classifying a mobile laser scanning (MLS) point cloud in urban areas. Initially, the point cloud is converted into a regular voxelized structure during a preprocessing step. This helps to overcome the challenge of irregularity and inhomogeneity. A density value is assigned to each voxel that describes the point distribution within the voxel’s location. Then, by training the designed CNN classifier, each point will receive the label of its corresponding voxel. The performance of the proposed D-Net method was tested using a point cloud dataset in an urban area. Our results demonstrated a relatively high level of performance with an overall accuracy (OA) of about 98% and precision, recall, and F1 scores of over 92%.

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Section: Deep Learning Based Methodsmentioning

confidence: 99%

D-Net: A Density-Based Convolutional Neural Network for Mobile LiDAR Point Clouds Classification in Urban Areas

et al. 2023

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“…EPC-Net [22] proposes a lightweight network module to aggregate the local geometric features for lower memory consumption. GSV-NET [23] converts the regions of the 3D point cloud into color representation and captures region features with a 2D wide-inception network.…”

Section: Related Workmentioning

confidence: 99%

PointVotes: A Deep Learing Point Cloud Model for Tire Bubble Defect Detection

Yang¹,

Jiang²,

Nie³

et al. 2022

cai

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In order to eliminate the hidden dangers caused by tire bubble defects, considering that the two-dimensional technology is sensitive to light, the 3D point cloud technology is used to obtain the tire surface morphology. This paper proposes a 3D point cloud network model named PointVotes, a point based target detection method. The designed structural framework includes: the fusion sampling layer, the voting layer and the proposal refinement layer. By observing the spatial characteristics of the detected target, a new point sampling method named C-farthest point sampling (C-FPS) is proposed. Combining with the fusion sampling strategy, the FPS and the C-FPS are sampled in a certain proportion. It solves the problem that the proposal box cannot be generated due to less available prospect information when generating suggestions for small targets. The network model uses Set Abstraction layers in multiple PointNet++ to extract features, arranges and combines features of different scales, forms high-dimensional features of points and votes, judges whether there are bubble defects through classification, and then generates proposals and regression to the prediction frame. Experiment results show that * Corresponding author PointVotes 1447 the mean average precision of the model can reach 82.8 % with a detection time of 0.12 s.

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“…In recent years, artificial neural networks (ANNs) designed to process point cloud data have been intensively developed. Examples of this include PointNet, PointNet++, PointCNN, PointSeg, LaserNet, VoxNet, SEGCloud, LGENet, and GSV-NET [4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation and Building Extraction from Airborne LiDAR Data with Multiple Return Using PointNet++

2022

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Light detection and ranging (LiDAR) data of 3D point clouds acquired from laser sensors is a crucial form of geospatial data for recognition of complex objects since LiDAR data provides geometric information in terms of 3D coordinates with additional attributes such as intensity and multiple returns. In this paper, we focused on utilizing multiple returns in the training data for semantic segmentation, in particular building extraction using PointNet++. PointNet++ is known as one of the efficient and robust deep learning (DL) models for processing 3D point clouds. On most building boundaries, two returns of the laser pulse occur. The experimental results demonstrated that the proposed approach could improve building extraction by adding two returns to the training datasets. Specifically, the recall value of the predicted building boundaries for the test data was improved from 0.7417 to 0.7948 for the best case. However, no significant improvement was achieved for the new data because the new data had relatively lower point density compared to the training and test data.

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GSV-NET: A Multi-Modal Deep Learning Network for 3D Point Cloud Classification

Cited by 13 publications

References 50 publications

D-Net: A Density-Based Convolutional Neural Network for Mobile LiDAR Point Clouds Classification in Urban Areas

D-Net: A Density-Based Convolutional Neural Network for Mobile LiDAR Point Clouds Classification in Urban Areas

PointVotes: A Deep Learing Point Cloud Model for Tire Bubble Defect Detection

Semantic Segmentation and Building Extraction from Airborne LiDAR Data with Multiple Return Using PointNet++

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