Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to est...Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.展开更多
The classification of point cloud data is the key technology of point cloud data information acquisition and 3D reconstruction, which has a wide range of applications. However, the existing point cloud classification ...The classification of point cloud data is the key technology of point cloud data information acquisition and 3D reconstruction, which has a wide range of applications. However, the existing point cloud classification methods have some shortcomings when extracting point cloud features, such as insufficient extraction of local information and overlooking the information in other neighborhood features in the point cloud, and not focusing on the point cloud channel information and spatial information. To solve the above problems, a point cloud classification network based on graph convolution and fusion attention mechanism is proposed to achieve more accurate classification results. Firstly, the point cloud is regarded as a node on the graph, the k-nearest neighbor algorithm is used to compose the graph and the information between points is dynamically captured by stacking multiple graph convolution layers;then, with the assistance of 2D experience of attention mechanism, an attention mechanism which has the capability to integrate more attention to point cloud spatial and channel information is introduced to increase the feature information of point cloud, aggregate local useful features and suppress useless features. Through the classification experiments on ModelNet40 dataset, the experimental results show that compared with PointNet network without considering the local feature information of the point cloud, the average classification accuracy of the proposed model has a 4.4% improvement and the overall classification accuracy has a 4.4% improvement. Compared with other networks, the classification accuracy of the proposed model has also been improved.展开更多
Data augmentation is a widely used regularization strategy in deep neural networks to mitigate overfitting and enhance generalization.In the context of point cloud data,mixing two samples to generate new training exam...Data augmentation is a widely used regularization strategy in deep neural networks to mitigate overfitting and enhance generalization.In the context of point cloud data,mixing two samples to generate new training examples has proven to be effective.In this paper,we propose a novel and effective approach called Farthest Point Sampling Mix(FPSMix)for augmenting point cloud data.Our method leverages farthest point sampling,a technique used in point cloud processing,to generate new samples by mixing points from two original point clouds.Another key innovation of our approach is the introduction of a significance-based loss function,which assigns weights to the soft labels of the mixed samples based on the classification loss of each part of the new sample that is separated from the two original point clouds.This way,our method takes into account the importance of different parts of the mixed sample during the training process,allowing the model to learn better global features.Experimental results demonstrate that our FPSMix,combined with the significance-based loss function,improves the classification accuracy of point cloud models and achieves comparable performance with state-of-the-art data augmentation methods.Moreover,our approach is complementary to techniques that focus on local features,and their combined use further enhances the classification accuracy of the baseline model.展开更多
Precise classification of Light Detection and Ranging(LiDAR)point cloud is a fundamental process in various applications,such as land cover mapping,forestry management,and autonomous driving.Due to the lack of spectra...Precise classification of Light Detection and Ranging(LiDAR)point cloud is a fundamental process in various applications,such as land cover mapping,forestry management,and autonomous driving.Due to the lack of spectral information,the existing research on single wavelength LiDAR classification is limited.Spectral information from images could address this limitation,but data fusion suffers from varying illumination conditions and the registration problem.A novel multispectral LiDAR successfully obtains spatial and spectral information as a brand-new data type,namely,multispectral point cloud,thereby improving classification performance.However,spatial and spectral information of multispectral LiDAR has been processed separately in previous studies,thereby possibly limiting the classification performance of multispectral LiDAR.To explore the potential of this new data type,the current spatial-spectral classification framework for multispectral LiDAR that includes four steps:(1)neighborhood selection,(2)feature extraction and selection,(3)classification,and(4)label smoothing.Three novel highlights were proposed in this spatial-spectral classification framework.(1)We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood.(2)We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy.(3)We conducted spatial label smoothing by a conditional random field,accounting for the spatial and spectral information of the neighborhood points.The proposed method demonstrated by a multispectral LiDAR with three channels:466 nm(blue),527 nm(green),and 628 nm(red).Experimental results demonstrate the effectiveness of the proposed spatial-spectral classification framework.Moreover,this research takes advantages of the complementation of spatial and spectral information,which could benefit more precise neighborhood selection,more effective features,and satisfactory refinement of classification result.Finally,this study could serve as an inspiration for future efficient spatial-spectral process for multispectral point cloud.展开更多
基金supported in part by the Nationa Natural Science Foundation of China (61876011)the National Key Research and Development Program of China (2022YFB4703700)+1 种基金the Key Research and Development Program 2020 of Guangzhou (202007050002)the Key-Area Research and Development Program of Guangdong Province (2020B090921003)。
文摘Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://github.com/yahuiliu99/PointC onT.
文摘The classification of point cloud data is the key technology of point cloud data information acquisition and 3D reconstruction, which has a wide range of applications. However, the existing point cloud classification methods have some shortcomings when extracting point cloud features, such as insufficient extraction of local information and overlooking the information in other neighborhood features in the point cloud, and not focusing on the point cloud channel information and spatial information. To solve the above problems, a point cloud classification network based on graph convolution and fusion attention mechanism is proposed to achieve more accurate classification results. Firstly, the point cloud is regarded as a node on the graph, the k-nearest neighbor algorithm is used to compose the graph and the information between points is dynamically captured by stacking multiple graph convolution layers;then, with the assistance of 2D experience of attention mechanism, an attention mechanism which has the capability to integrate more attention to point cloud spatial and channel information is introduced to increase the feature information of point cloud, aggregate local useful features and suppress useless features. Through the classification experiments on ModelNet40 dataset, the experimental results show that compared with PointNet network without considering the local feature information of the point cloud, the average classification accuracy of the proposed model has a 4.4% improvement and the overall classification accuracy has a 4.4% improvement. Compared with other networks, the classification accuracy of the proposed model has also been improved.
基金supported by the National Key R&D Program of China(No.2020YFB1708002)the National Natural Science Foundation of China(Grant Nos.62371009 and 61971008).
文摘Data augmentation is a widely used regularization strategy in deep neural networks to mitigate overfitting and enhance generalization.In the context of point cloud data,mixing two samples to generate new training examples has proven to be effective.In this paper,we propose a novel and effective approach called Farthest Point Sampling Mix(FPSMix)for augmenting point cloud data.Our method leverages farthest point sampling,a technique used in point cloud processing,to generate new samples by mixing points from two original point clouds.Another key innovation of our approach is the introduction of a significance-based loss function,which assigns weights to the soft labels of the mixed samples based on the classification loss of each part of the new sample that is separated from the two original point clouds.This way,our method takes into account the importance of different parts of the mixed sample during the training process,allowing the model to learn better global features.Experimental results demonstrate that our FPSMix,combined with the significance-based loss function,improves the classification accuracy of point cloud models and achieves comparable performance with state-of-the-art data augmentation methods.Moreover,our approach is complementary to techniques that focus on local features,and their combined use further enhances the classification accuracy of the baseline model.
基金supported by the National Natural Science Foundation of China[grant number 41971307]Fundamental Research Funds for the Central Universities[grant number 2042022kf1200,2042023kf0217]+1 种基金Wuhan University Specific Fund for Major School-level Internationalization InitiativesLIESMARS Special Research Funding.
文摘Precise classification of Light Detection and Ranging(LiDAR)point cloud is a fundamental process in various applications,such as land cover mapping,forestry management,and autonomous driving.Due to the lack of spectral information,the existing research on single wavelength LiDAR classification is limited.Spectral information from images could address this limitation,but data fusion suffers from varying illumination conditions and the registration problem.A novel multispectral LiDAR successfully obtains spatial and spectral information as a brand-new data type,namely,multispectral point cloud,thereby improving classification performance.However,spatial and spectral information of multispectral LiDAR has been processed separately in previous studies,thereby possibly limiting the classification performance of multispectral LiDAR.To explore the potential of this new data type,the current spatial-spectral classification framework for multispectral LiDAR that includes four steps:(1)neighborhood selection,(2)feature extraction and selection,(3)classification,and(4)label smoothing.Three novel highlights were proposed in this spatial-spectral classification framework.(1)We improved the popular eigen entropy-based neighborhood selection by spectral angle match to extract a more precise neighborhood.(2)We evaluated the importance of geometric and spectral features to compare their contributions and selected the most important features to reduce feature redundancy.(3)We conducted spatial label smoothing by a conditional random field,accounting for the spatial and spectral information of the neighborhood points.The proposed method demonstrated by a multispectral LiDAR with three channels:466 nm(blue),527 nm(green),and 628 nm(red).Experimental results demonstrate the effectiveness of the proposed spatial-spectral classification framework.Moreover,this research takes advantages of the complementation of spatial and spectral information,which could benefit more precise neighborhood selection,more effective features,and satisfactory refinement of classification result.Finally,this study could serve as an inspiration for future efficient spatial-spectral process for multispectral point cloud.
