Dynamic-Scale Graph Convolutional Network for Semantic Segmentation of 3D Point Cloud

Xiu, Haoyi; Shinohara, Takayuki; Matsuoka, M.

doi:10.1109/ism46123.2019.00062

Cited by 8 publications

(4 citation statements)

References 16 publications

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“…2 Complexity operator with respect to the graph in the spectral domain is an important approach [47][48][49], but it needs to calculate a lot of parameters on polynomial or rational spectral filters [50]. Recently, many researchers constructed local graph of point cloud by utilizing each point's neighbors in low-dimensional manifold based on N-dimensional Euclidean distance and then grouped each point's neighbors in the form of highdimensional vectors, such as EdgeConv-like works [26,27,51] and graph convolutions [37,52]. Compared with the spectral methods, its main merit is that it is more consistent with the characteristics of data distribution.…”

Section: Graph Convolutional Network Graph Convolutionalmentioning

confidence: 99%

Multiscale Receptive Fields Graph Attention Network for Point Cloud Classification

Wang

Lü³

2021

Complexity

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Understanding the implication of point cloud is still challenging in the aim of classification or segmentation for point cloud due to its irregular and sparse structure. As we have known, PointNet architecture as a ground-breaking work for point cloud process can learn shape features directly on unordered 3D point cloud and has achieved favorable performance, such as 86% mean accuracy and 89.2% overall accuracy for classification task, respectively. However, this model fails to consider the fine-grained semantic information of local structure for point cloud. Then, a multiscale receptive fields graph attention network (named after MRFGAT) by means of semantic features of local patch for point cloud is proposed in this paper, and the learned feature map for our network can well capture the abundant features information of point cloud. The proposed MRFGAT architecture is tested on ModelNet datasets, and results show it achieves state-of-the-art performance in shape classification tasks, such as it outperforms GAPNet (Chen et al.) model by 0.1% in terms of OA and compete with DGCNN (Wang et al.) model in terms of MA.

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Section: Graph Convolutional Network Graph Convolutionalmentioning

confidence: 99%

Multiscale Receptive Fields Graph Attention Network for Point Cloud Classification

Wang

Lü³

2021

Complexity

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“…To address these problems of the information lost in the translation process, a CNN-like network called PointNet was proposed to handle 3D point clouds. Additionally, some studies have applied CNN based techniques to irregular point clouds [ 37 , 38 , 39 , 40 , 41 , 42 ] after PointNet was proposed. These methods offer an integrated architecture that avoids high computational costs coming with high resolution voxels and allows point cloud data to be entered directly for semantic segmentation tasks.…”

Section: Related Studiesmentioning

confidence: 99%

FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning

Shinohara

Xiu

Matsuoka

2020

Sensors

Self Cite

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In the computer vision field, many 3D deep learning models that directly manage 3D point clouds (proposed after PointNet) have been published. Moreover, deep learning-based-techniques have demonstrated state-of-the-art performance for supervised learning tasks on 3D point cloud data, such as classification and segmentation tasks for open datasets in competitions. Furthermore, many researchers have attempted to apply these deep learning-based techniques to 3D point clouds observed by aerial laser scanners (ALSs). However, most of these studies were developed for 3D point clouds without radiometric information. In this paper, we investigate the possibility of using a deep learning method to solve the semantic segmentation task of airborne full-waveform light detection and ranging (lidar) data that consists of geometric information and radiometric waveform data. Thus, we propose a data-driven semantic segmentation model called the full-waveform network (FWNet), which handles the waveform of full-waveform lidar data without any conversion process, such as projection onto a 2D grid or calculating handcrafted features. Our FWNet is based on a PointNet-based architecture, which can extract the local and global features of each input waveform data, along with its corresponding geographical coordinates. Subsequently, the classifier consists of 1D convolutional operational layers, which predict the class vector corresponding to the input waveform from the extracted local and global features. Our trained FWNet achieved higher scores in its recall, precision, and F1 score for unseen test data—higher scores than those of previously proposed methods in full-waveform lidar data analysis domain. Specifically, our FWNet achieved a mean recall of 0.73, a mean precision of 0.81, and a mean F1 score of 0.76. We further performed an ablation study, that is assessing the effectiveness of our proposed method, of the above-mentioned metric. Moreover, we investigated the effectiveness of our PointNet based local and global feature extraction method using the visualization of the feature vector. In this way, we have shown that our network for local and global feature extraction allows training with semantic segmentation without requiring expert knowledge on full-waveform lidar data or translation into 2D images or voxels.

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“…But, it needs to calculate a lot of parameters on polynomial or rational spectral filters [46]. Recent, many researchers constructed local graph by applying each point's neighbors in embedding space based on N -dimensional Euclidean distance, then grouped each point's neighbors in the form of high dimensional vectors, such as EdgeConv-like works [15,23,47] and graph convolutions [34,48]. Compared with the spectral methods, its main merit is that it is more consistent with the characteristics of data distribution.…”

Section: Graph Convolutional Networkmentioning

confidence: 99%

Multi-scale Receptive Fields Graph Attention Network for Point Cloud Classification

Zhang

Wang³

et al. 2020

Preprint

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Understanding the implication of point cloud is still challenging to achieve the goal of classification or segmentation due to the irregular and sparse structure of point cloud. As we have known, PointNet architecture as a ground-breaking work for point cloud which can learn efficiently shape features directly on unordered 3D point cloud and have achieved favorable performance. However, this model fail to consider the fine-grained semantic information of local structure for point cloud. Afterwards, many valuable works are proposed to enhance the performance of PointNet by means of semantic features of local patch for point cloud. In this paper, a multi-scale receptive fields graph attention network (named after MRFGAT) for point cloud classification is proposed. By focusing on the local fine features of point cloud and applying multi attention modules based on channel affinity, the learned feature map for our network can well capture the abundant features information of point cloud. The proposed MRFGAT architecture is tested on ModelNet10 and ModelNet40 datasets, and results show it achieves state-of-the-art performance in shape classification tasks.

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Dynamic-Scale Graph Convolutional Network for Semantic Segmentation of 3D Point Cloud

Cited by 8 publications

References 16 publications

Multiscale Receptive Fields Graph Attention Network for Point Cloud Classification

Multiscale Receptive Fields Graph Attention Network for Point Cloud Classification

FWNet: Semantic Segmentation for Full-Waveform LiDAR Data Using Deep Learning

Multi-scale Receptive Fields Graph Attention Network for Point Cloud Classification

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