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收稿：2020-06-09，
修回：2020-9-27，
录用：2020-10-4，
纸质出版：2021-11-16
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目的
2
在点云分类处理的各环节中，关键是准确描述点云的局部邻域结构并提取表达能力强的点云特征集合。为了改进传统邻域结构单尺度特征表达能力的有限性和多尺度特征的计算复杂性，本文提出了用于激光点云分类的稀疏体素金字塔邻域结构及对应的分类方法。
方法
2
通过对原始数据进行不同尺度下采样构建稀疏体素金字塔，并根据稀疏体素金字塔提取多尺度特征，利用随机森林分类器进行初始分类；构建无向图，利用直方图交集核计算邻域点之间连接边的权重，通过多标签图割算法优化分类结果。当体素金字塔的接收域增大时，邻域点密度随其距离中心点距离的增加而减小，有效减少了计算量。
结果
2
在地基Semantic3D数据集、车载点云数据和机载点云数据上进行实验，结果表明，在降低计算复杂性的前提下，本文方法的分类精度、准确性和鲁棒性达到了同类算法前列，验证了该框架作为点云分类基础框架的有效性。
结论
2
与类似方法相比，本文方法提取的多尺度特征既保持了点的局部结构信息，也更好地兼顾了较大尺度的点云结构特征，因而提升了点云分类的精度。
Objective
2
Point cloud classification is one of the hotspots of computer vision research. Among of various kinds of processing stages
accurately describing the local neighborhood structure of the point cloud and extracting the point cloud feature sets with strong expressive ability has become the key to point cloud classification. Traditionally
two methods can be used for modeling the neighborhood structure of point clouds: single-scale description and multiscale description. The former has a limited expressive ability
whereas the latter has a strong description ability but comes with a high computational complexity. To solve the above problems
this paper proposes a sparse voxel pyramid structure to express the local neighborhood structure of the point cloud and provides the corresponding point cloud classification and optimization method.
Method
2
First
after a comparative analysis of related point cloud classification methods
the paper describes in detail the structure of the proposed sparse voxel pyramid
analyzes the advantages of the sparse voxel pyramid in expressing the neighborhood structure of the point cloud
and provides the method to express the local neighborhood of the point could with this structure. When calculating point features
the influence of candidate points on the local feature calculation results gradually decreases as the distance decreases. Thus
a fixed number of neighbors is used to construct each layer of the sparse voxel pyramid. For each voxel
a sparse voxel pyramid of
N
layers is constructed
and the voxel radius of the 0th layer is set to
R
. The value of
N
can be set according to the computing power of hardware resources. The
R
value is the smallest voxel value in the entire voxel pyramid
and its size can be set according to the point cloud density and range of the scene. The voxel radius of each subsequent layer of the pyramid is in turn twice that of the previous layer. The voxel radius of the
N
th layer is 2
N
R
and each layer contains the same number of
K
voxels. Each point in the original point cloud is built into a spatial K-nearest neighbor index in voxel point clouds of different scales to form a sparse voxel pyramid by downsampling the original point cloud according to the above-mentioned proportions. This method can determine the multiscale neighborhood of the center point only based on a fixed-size
K
value. Near the center point
the point cloud density maintains the original density. As the distance from the center point increases
the density of neighboring points becomes sparser. Based on the sparse voxel pyramid structure
the local neighborhood constructed by points at different scales is used to extract feature-value-based features
neighborhood geometric features
projection features
and fast point feature histogram features of corresponding points. Then
the single point features are aggregated
the random forest method is used for supervised classification
and then the multilabel graph cut method is used to optimize the above classification results. After calculating the fast point feature histogram feature of each point
the histogram intersection core is used to calculate the edge potential between neighboring points.
Result
2
This paper selects three public datasets for experiments
namely
the ground-based Semantic3D dataset
the airborne LiDAR scanning data obtained by the airborne laser scanning system ALTM 2050 in different regions
and point cloud dataset at the main entrance of the Munich Technical University campus in Arcisstrasse by mobile LiDAR system
to verify the effectiveness of this method. The evaluation indicators used in the experiment are accuracy
recall
and F1 value. Sparse voxel pyramids of different scales are used for feature extraction and feature vector aggregation owing to the difference in point cloud density and coverage. Using the method proposed in this paper
the overall classification accuracy of the experimental results on the ground Semantic3D dataset can reach 89%
the classification accuracy of the airborne LiDAR scan dataset can reach 96%
and the classification accuracy of the mobile LiDAR scan dataset can reach 89%. Experimental results show that compared with other comparison methods
the multiscale features based on sparse voxel pyramids proposed can express the local structure of point clouds more effectively and robustly. When the receiving field of the voxel pyramid increases
the density of neighboring points decreases as the distance from the center point increases
which effectively reduces the amount of calculation. In addition
the histogram feature of fast point feature is used to calculate the difference between adjacent points through the histogram intersection kernel
which is used as the weight of the edge in the structural graph model to improve the optimization effect. This method is more accurate than the traditional method that uses Euclidean distance as the weight. The multilabel graph cut method can further optimize the results of single-point classification and provide better optimization results on issues such as the incorrect classification of vegetation into buildings and vice versa. In areas with large natural terrain undulations
the classification accuracy of terrain and low vegetation is greatly affected by the undulations of natural terrain in different regions
and misclassification easily occurs. By contrast
the classification accuracy on higher features such as tall vegetation
buildings
and pedestrians is less affected by the terrain
and the accuracy is higher.
Conclusion
2
Overall
compared with other similar and more advanced methods
the multiscale features extracted by the proposed method maintain the local structure information while considering a larger range of point cloud structure information
thereby improving the point cloud classification accuracy.
Ali W, Abdelkarim S, Zahran M, Zidan M and Sallab A E. 2018. YOLO3D: end-to-end real-time 3D oriented object bounding box detection from LiDAR point cloud//Proceedings of 2018 European Conference on Computer Vision. Munich, Germany: Springer: #11131[ DOI: doi.org/10.1007/978-3-030-11015-4_54 http://dx.doi.org/10.1007/978-3-030-11015-4_54 ]
Bassier M, Van Genechten B and Vergauwen M. 2019. Classification of sensor independent point cloud data of building objects using random forests. Journal of Building Engineering, 21: 468-477[DOI: 10.1016/j.jobe.2018.04.027]
Boykov Y, Veksler O and Zabih R. 2001. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11): 1222-1239[DOI: 10.1109/34.969114]
Breiman L. 2001. Random Forests. Machine learning. 45(1): 5-32
Charles R Q, Su H, Kaichun M and Guibas L J. 2017. PointNet: deep learning on point sets for 3D classification and segmentation//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE: 77-85[ DOI: 10.1109/CVPR.2017.16 http://dx.doi.org/10.1109/CVPR.2017.16 ]
Chen D, Zhang L Q, Mathiopoulos T and Huang X F. 2014. A methodology for automated segmentation and reconstruction of urban 3-D buildings from ALS point clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10): 4199-4217[DOI: 10.1109/JSTARS.2014.2349003]
Cheng M, Zhang H C, Wang C and Li J. 2017. Extraction and classification of road markings using mobile laser scanning point clouds. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(3): 1182-1196[DOI: 10.1109/JSTARS.2016.2606507]
DemantkéJ, Mallet C, David N and Vallet B. 2011. Dimensionality based scale selection in 3D LiDAR point clouds//Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Calgary, Canada: ISPRS: 97-102[ DOI: 10.5194/isprsarchives-XXXVIII-5-W12-97-2011 http://dx.doi.org/10.5194/isprsarchives-XXXVIII-5-W12-97-2011 ]
Dittrich A, Weinmann M and Hinz S. 2017. Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data. ISPRS Journal of Photogrammetry and Remote Sensing, 126: 195-208[DOI: 10.1016/j.isprsjprs.2017.02.012]
Filin S and Pfeifer N. 2005. Neighborhood systems for airborne laser data. Photogrammetric Engineering and Remote Sensing, 71(6): 743-755[DOI: 10.14358/PERS.71.6.743]
Grauman K and Darrell T. 2005. The pyramid match kernel: discriminative classification with sets of image features//Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China: IEEE: 1458-1465[ DOI: 10.1109/ICCV.2005.239 http://dx.doi.org/10.1109/ICCV.2005.239 ]
Grilli E, Menna F and Remondino F. 2017. A review of point clouds segmentation and classification algorithms//Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Nafplio, Greece: ISPRS: 339-344[ DOI: 10.5194/isprs-archives-XLII-2-W3-339-2017 http://dx.doi.org/10.5194/isprs-archives-XLII-2-W3-339-2017 ]
Guo B, Huang X F, Zhang F and Sohn G. 2015. Classification of airborne laser scanning data using JointBoost. ISPRS Journal of Photogrammetry and Remote Sensing, 100: 71-83[DOI: 10.1016/j.isprsjprs.2014.04.015]
Guo Y L, Sohel F A, Bennamoun M, Wan J W and Lu M. 2013. RoPS: a local feature descriptor for 3D rigid objects based on rotational projection statistics//Proceedings of the 1st International Conference on Communications, Signal Processing, and Their Applications. Sharjah, United Arab Emirates: IEEE: 1-6[ DOI: 10.1109/ICCSPA.2013.6487310 http://dx.doi.org/10.1109/ICCSPA.2013.6487310 ]
Hackel T, Savinov N, Ladicky L, Wegner J D, Schindler K and Pollefeys M. 2017. Semantic3D. net: a new large-scale point cloud classification benchmark//Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Hannover, Germany: ISPRS: 91-98[DOI: 10.5194/isprs-annals-IV-1-W1-91-2017 http://dx.doi.org/10.5194/isprs-annals-IV-1-W1-91-2017 ]
Kang Z Z, Yang J T and Zhong R F. 2017. A bayesian-network-based classification method integrating airborne LiDAR data with optical images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(4): 1651-1661[DOI: 10.1109/JSTARS.2016.2628775]
Landrieu L and Simonovsky M. 2017. Large-scale point cloud semantic segmentation with superpoint graphs//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE: 4558-4567[ DOI: 10.1109/CVPR.2018.00479 http://dx.doi.org/10.1109/CVPR.2018.00479 ]
Laube P, Franz M O and Umlauf G. 2017. Evaluation of features for SVM-based classification of geometric primitives in point clouds//Proceedings of the 15th IAPR International Conference on Machine Vision Applications(MVA). Nagoya, Greece: IEEE: 59-62[ DOI: 10.23919/MVA.2017.7986776 http://dx.doi.org/10.23919/MVA.2017.7986776 ]
Lee I and Schenk T. 2002. Perceptual organization of 3D surface points//International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 34(3/A): 193-198
Li Y Y, Bu R, Sun M C, Wu W, Di X H and Chen B Q. 2018. PointCNN: convolution on X -transformed points//Proceedings of Advances in Neural Information Processing Systems. Montréal, Canada: 820-830
Lim E H and Suter D. 2007. Conditional random field for 3D point clouds with adaptive data reduction//Proceedings of 2007 International Conference on Cyberworlds. Hannover, Germany: IEEE: 404-408[ DOI: 10.1109/CW.2007.30 http://dx.doi.org/10.1109/CW.2007.30 ]
Lim E H and Suter D. 2009. 3D terrestrial LIDAR classifications with super-voxels and multi-scale Conditional Random Fields. Computer-Aided Design, 41(10): 701-710[DOI: 10.1016/j.cad.2009.02.010]
Linsen L and Prautzsch H. 2001. Local versus global triangulations//Chalmers A and Rhyne T M, eds. Eurographics 2001. Oxford: Eurographics Association
Liu Z Q, Li P C, Chen X W, Zhang B M and Guo H T. 2016. Classification of airborne LiDAR point cloud data based on information vector machine. Optics and Precision Engineering, 24(1): 210-219
刘志青, 李鹏程, 陈小卫, 张保明, 郭海涛. 2016. 基于信息向量机的机载激光雷达点云数据分类. 光学精密工程, 24(1): 210-219 [DOI: 10.3788/OPE.20162401.0210]
Luo H, Wang C, Wen C L, Chen Z Y, Zai D W, Yu Y T and Li J. 2018. Semantic labeling of mobile LiDAR point clouds via active learning and higher order MRF. IEEE Transactions on Geoscience and Remote Sensing, 56(7): 3631-3644[DOI: 10.1109/TGRS.2018.2802935]
Mitra N J, Nguyen A and Guibas L. 2004. Estimating surface normals in noisy point cloud data. International Journal of Computational Geometry and Applications, 14(4/5): 261-276[DOI: 10.1142/S0218195904001470]
Najafi M, Namin S T, Salzmann M and Petersson L. 2014. Non-associative higher-order Markov networks for point cloud classification//Proceedings of 2014 European Conference on Computer Vision. Zurich, Switzerland: Spnhger: 500-515[ DOI: doi.org/10.1007/978-3-319-10602-1_33 http://dx.doi.org/10.1007/978-3-319-10602-1_33 ]
Ni H, Lin X G and Zhang J X. 2017. Classification of ALS point cloud with improved point cloud segmentation and random forests. Remote Sensing, 9(3): 288[DOI: 10.3390/rs9030288]
Niemeyer J, Rottensteiner F and Soergel U. 2012. Conditional random fields for LIDAR point cloud classification in complex urban areas//Proceedings of the ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Melbourne, Australia: ISPRS: 263-268[ DOI: 10.5194/isprsannals-I-3-263-2012 http://dx.doi.org/10.5194/isprsannals-I-3-263-2012 ]
Qi C R, Yi L, Su H and Guibas L J. 2017. PointNet++: deep hierarchical feature learning on point sets in a metric space//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, USA: Curran Associates Inc: 5105-5114
Rusu R B, Marton Z C, Blodow N and Beetz M. 2008. Persistent point feature histograms for 3D point clouds. Intelligent Autonomous Systems 10. Burgard W. et al (Eds. ). IOS Press[ DOI: 10.3233/978-1-58603-887-8-119 http://dx.doi.org/10.3233/978-1-58603-887-8-119 ]
Rusu R B, Blodow N and Beetz M. 2009. Fast Point Feature Histograms (FPFH) for 3D registration//Proceedings of 2009 IEEE International Conference on Robotics and Automation. Kobe, Japan: IEEE: 3212-3217[ DOI: 10.1109/ROBOT.2009.5152473 http://dx.doi.org/10.1109/ROBOT.2009.5152473 ]
Shapovalov R and Velizhev A. 2011. Cutting-plane training of non-associative Markov network for 3D point cloud segmentation//Proceedings of 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission. Hangzhou, China: IEEE: 1-8[ DOI: 10.1109/3DIMPVT.2011.10 http://dx.doi.org/10.1109/3DIMPVT.2011.10 ]
Tombari F, Salti S and Di Stefano L. 2010. Unique signatures of histograms for local surface description//Proceedings of the 11th European Conference on Computer Vision. Heraklion, Greece: Springer: 356-369[ DOI: 10.1007/978-3-642-15558-1_26 http://dx.doi.org/10.1007/978-3-642-15558-1_26 ]
Wang C, Hou S W, Wen C L, Gong Z, Li Q, Sun X T and Li J. 2018a. Semantic line framework-based indoor building modeling using backpacked laser scanning point cloud. ISPRS Journal of Photogrammetry and Remote Sensing, 143: 150-166[DOI: 10.1016/j.isprsjprs.2018.03.025]
Wang Z, Zhang L Q, Zhang L, Li R J, Zheng Y B and Zhu Z D. 2018b. A deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification. IEEE Transactions on Geoscience and Remote Sensing, 56(8): 4594-4604[DOI: 10.1109/TGRS.2018.2829625]
Weinmann M, Jutzi B and Mallet C. 2014. Semantic 3D scene interpretation: a framework combining optimal neighborhood size selection with relevant features//Proceedings of SPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Zurich, Switzerland: ISPRS: 181-188[ DOI: 10.5194/isprsannals-II-3-181-2014 http://dx.doi.org/10.5194/isprsannals-II-3-181-2014 ]
Weinmann M, Jutzi B, Hinz S and Mallet C. 2015b. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS Journal of Photogrammetry and Remote Sensing, 105: 286-304[DOI: 10.1016/j.isprsjprs.2015.01.016]
Weinmann Ma, Jutzi B, Mallet C and Weinmann Mi. 2017. Geometric features and their relevance for 3 d point cloud classification. ISPRS Annals of Photogrammetry, Remote Sensing&Spatial Information Sciences, 2017, 4: 157-164[DOI: 10.5194/isprs-annals-IV-1-W1-157-2017]
Weinmann M, Urban S, Hinz S, Jutzi B and Mallet C. 2015a. Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas. Computers and Graphics, 49: 47-57[DOI: 10.1016/j.cag.2015.01.006]
West K F, Webb B N, Lersch J R, Pothier S, Triscari J M and Iverson A E. 2004. Context-driven automated target detection in 3D data//Proceedings of SPIE 5426, Automatic Target Recognition XIV. Orlando, USA: SPIE: 133-143[ DOI: 10.1117/12.542536 http://dx.doi.org/10.1117/12.542536 ]
Yang B S and Dong Z. 2013. A shape-based segmentation method for mobile laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 81: 19-30[DOI: 10.1016/j.isprsjprs.2013.04.002]
Yang B S, Dong Z, Zhao G and Dai W X. 2015. Hierarchical extraction of urban objects from mobile laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 99: 45-57[DOI: 10.1016/j.isprsjprs.2014.10.005]
Yang J T and Kang Z Z. 2018. Multi-scale feature and Markov random field model for power line scene point cloud classification. Journal of Surveying and Mapping, 2: 188-197
杨俊涛, 康志忠. 2018. 多尺度特征和马尔可夫随机场模型的电力线场景点云分类法. 测绘学报, 2: 188-197 [DOI: 10.11947/j.AGCS.2018.20170556]
Zhi S F, Liu Y X, Li X and Guo Y L. 2017. LightNet: a lightweight 3D convolutional neural network for real-time 3D object recognition//Pratikakis I, Dupont F and Ovsjanikov M, eds. Eurographics Workshop on 3D Object Retrieval. Lyon: the Eurographics Association: 9-16[ DOI:10.2312/3dor.20171046 http://dx.doi.org/10.2312/3dor.20171046 ]
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