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纸质出版日期： 2021-01-07 ，
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杨必胜，韩旭，董震.2021.点云深度学习基准数据集.遥感学报，25（1）： 231-240
Yang B S，Han X and Dong Z. 2021. Point cloud benchmark dataset WHU-TLS and WHU-MLS for deep learning. National Remote Sensing Bulletin， 25（1）：231-240
为推进深度学习方法在点云配准、语义分割、实例分割等领域的发展，武汉大学联合国内外多家高等院校和研究机构发布了包含多类型场景的地面站点云配准基准数据集WHU-TLS和包含语义、实例的城市级车载点云基准数据集WHU-MLS。其中，WHU-TLS基准数据集涵盖了地铁站、高铁站、山地、公园、校园、住宅、河岸、文化遗产建筑、地下矿道、隧道等10种不同的环境，共包含115个测站、17.4亿个三维点以及点云之间的真实转换矩阵，为点云配准提供了迄今为止最大规模的基准数据集。WHU-MLS基准数据集涵盖了地面特征（机动车道、道路标线、井盖、非机动车道），动态目标（行人、车辆），植被（树木、树丛、低矮植被），杆状地物及其附属结构（电线杆、独立提示牌、路灯、信号灯、独立探头等），建筑和结构设施（房屋、道路隔离结构、围墙和栅栏）以及其他公共和便利设施（垃圾桶、邮筒、消防栓、街头座椅、电力线等）等6大类30余小类地物要素，共包含2亿多个点和超过5000个实例对象，为语义分割、实例分割点云深度学习网络的训练、测试和性能评估提供了当前最为丰富的基准数据集。
This paper aims to elaborate two large-scale point cloud benchmark datasets
namely
WHU-TLS and WHU-MLS
for deep learning purposes. The benchmark of the Whu-TLS data set comprises 115 scans and over 1740 million 3D points collected from 11 different environments (i.e.
subway station
high-speed railway platform
mountain
forest
park
campus
residence
riverbank
heritage building
underground excavation
and tunnel environments) with variations in the point density
clutter
and occlusion. The aims of the proposed benchmark are to facilitate better comparisons and provide insights into the strengths and weaknesses of different registration approaches based on a common standard.
The ground-truth transformations and registration graphs are also provided to allow researchers to evaluate their registration solutions and for environmental modeling. In addition
the Whu-TLS data set provides suitable data for applications in safe railway operation
river surveys and regulation
forest structure assessment
cultural heritage conservation
landslide monitoring
and underground asset management. WHU-MLS benchmark dataset includes more than 30 kinds of objects and 5000 typical instances in urban scene. We manually labeled MLS point cloud
each point with spatial coordinates and normal. We totally labeled 40 scenes with average number of points 8 million
of which 30 scenes are split for training and 10 scenes for testing.
The coarse and fine categories are defined as follows. The Construction: building (including the building façade and other clutters in the building)
fence (including isolation structure on the road and wall); Natural: trees
low vegetation
including grass
shrub and other low tree; Ground: driveway (not including road mark)
non-drive way
the ground that does not belong to the driveway
road markings; Dynamic: person (including person and bikes)
car; Pole: light
electric pole
municipal pole
signal light
detector
board (usually attached to the light). The semantic labeling and instance labeling in WHU-MLS provide important references for point cloud deep learning. On the one hand
these datasets can be used for point cloud deep learning networks the training
testing
and evaluation of point cloud deep learning networks. On the other hand
the benchmark datasets would can promote the benchmarking of state-of-the-art algorithms in this field
and ensure better comparisons on a common base. WHU-TLS and WHU-MLS are freely available can be used freely for scientific research. We hope that the Whu-TLS and Whu-MLS benchmark data sets meet the needs of the research community and becomes important data sets for the development of cutting-edge TLS point cloud registration and point cloud segmentation methods.
遥感深度学习配准语义分割实例分割点云基准数据集
remote sensingdeep learningregistrationsemantic segmentationinstance segmentationbenchmark
Behley J, Garbade M, Milioto A, Quenzel J, Behnke S, Stachniss C and Gall J. 2019. SemanticKITTI: a dataset for semantic scene understanding of liDAR sequences//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 9296-9306 [DOI: 10.1109/ICCV.2019.00939http://dx.doi.org/10.1109/ICCV.2019.00939]
Campbell M, Egerstedt M, How J P and Murray R M. 2010. Autonomous driving in urban environments: approaches, lessons and challenges. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1928): 4649-4672 [DOI: 10.1098/rsta.2010.0110http://dx.doi.org/10.1098/rsta.2010.0110]
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, HI, USA: IEEE: 77-85 [DOI: 10.1109/CVPR.2017.16http://dx.doi.org/10.1109/CVPR.2017.16]
Dai A, Chang A X, Savva M, Halber M, Funkhouser T and Nießner M. 2017. Scannet: richly-annotated 3D reconstructions of indoor scenes//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE: 2432-2443 [DOI: 10.1109/CVPR.2017.261http://dx.doi.org/10.1109/CVPR.2017.261]
Deng H W, Birdal T and Ilic S. 2018a. PPFNet: global context aware local features for robust 3D point matching//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE: 195-205 [DOI: 10.1109/CVPR.2018.00028http://dx.doi.org/10.1109/CVPR.2018.00028]
Deng H W, Birdal T and Ilic S. 2018b. PPF-foldnet: unsupervised learning of rotation invariant 3D local descriptors//Proceedings of the 15th European Conference on Computer Vision. Munich: Springer: 620-638 [DOI: 10.1007/978-3-030-01228-1_37http://dx.doi.org/10.1007/978-3-030-01228-1_37]
Deng H W, Birdal T and Ilic S. 2019. 3D local features for direct pairwise registration//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE: 3239-3248 [DOI: 10.1109/CVPR.2019.00336http://dx.doi.org/10.1109/CVPR.2019.00336]
Dong Z, Liang F X, Yang B S, Xu Y S, Zang Y F, Li J P, Wang Y, Dai W X, Fan H C, Hyyppä J and Stilla U. 2020. Registration of large-scale terrestrial laser scanner Point Clouds: a Review and Benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 163: 327-342 [DOI: 10.1016/j.isprsjprs.2020.03.013http://dx.doi.org/10.1016/j.isprsjprs.2020.03.013]
Dong Z, Yang B S, Liang F X, Huang R G and Scherer S. 2018. Hierarchical registration of unordered TLS point clouds based on binary shape context descriptor. ISPRS Journal of Photogrammetry and Remote Sensing, 144: 61-79 [DOI: 10.1016/j.isprsjprs.2018.06.018http://dx.doi.org/10.1016/j.isprsjprs.2018.06.018]
Dong Z, Yang B S, Liu Y, Liang F X, Li B J and Zang Y F. 2017. A novel binary shape context for 3D local surface description. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 431-452 [DOI: 10.1016/j.isprsjprs.2017.06.012http://dx.doi.org/10.1016/j.isprsjprs.2017.06.012]
Ge X M. 2017. Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 344-357 [DOI: 10.1016/j.isprsjprs.2017.06.011http://dx.doi.org/10.1016/j.isprsjprs.2017.06.011]
Geiger A, Lenz P, Stiller C and Urtasun R. 2013. Vision meets robotics: the kitti dataset. The International Journal of Robotics Research, 32(11): 1231-1237 [DOI: 10.1177/0278364913491297http://dx.doi.org/10.1177/0278364913491297]
Guo Y L, Sohel F, Bennamoun M, Lu M and Wan J W. 2013. Rotational projection statistics for 3D local surface description and object recognition. International Journal of Computer Vision, 105(1): 63-86 [DOI: 10.1007/s11263-013-0627-yhttp://dx.doi.org/10.1007/s11263-013-0627-y]
Han L, Zheng T, Xu L and Fang L. 2020. Occuseg: occupancy-aware 3D instance segmentation//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE: 2937-2946 [DOI: 10.1109/CVPR42600.2020.00301http://dx.doi.org/10.1109/CVPR42600.2020.00301]
Hu Q Y, Yang B, Xie L H, Rosa S, Guo Y L, Wang Z H, Trigoni N and Markham A. 2020. RandLA-Net: efficient semantic segmentation of large-scale point clouds//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE: 11105-11114 [DOI: 10.1109/CVPR42600.2020.01112http://dx.doi.org/10.1109/CVPR42600.2020.01112]
Jiang L, Zhao H S, Shi S S, Liu S, Fu C W and Jia J Y. 2020. PointGroup: dual-set point grouping for 3D instance segmentation//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, USA: IEEE: 4866-4875 [DOI: 10.1109/CVPR42600.2020.00492http://dx.doi.org/10.1109/CVPR42600.2020.00492]
Levinson J, Askeland J, Becker J, Dolson J, Held D, Kammel S, Kolter J Z, Langer D, Pink O, Pratt V, Sokolsky M, Stanek G and Stavens D. 2011. Towards fully autonomous driving: Systems and algorithms//Proceedings of 2011 IEEE Intelligent Vehicles Symposium. Baden-Baden, Germany: IEEE: 163-168 [DOI: 10.1109/IVS.2011.5940562http://dx.doi.org/10.1109/IVS.2011.5940562]
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.2009.5152473http://dx.doi.org/10.1109/ROBOT.2009.5152473]
Tan W K, Qin N N, Ma L F, Li Y, Du J, Cai G R, Yang K and Li J. 2020. Toronto-3D: a large-scale mobile lidar dataset for semantic segmentation of urban roadways//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle, WA, USA: IEEE: 797-806 [DOI: 10.1109/CVPRW50498.2020.00109http://dx.doi.org/10.1109/CVPRW50498.2020.00109]
Torralba A and Efros A A, 2011. Unbiased look at dataset bias//Proceedings of CVPR 2011. Providence, RI, USA: IEEE: 1521-1528 [DOI: 10.1109/CVPR.2011.5995347http://dx.doi.org/10.1109/CVPR.2011.5995347]
Wang L, Huang Y C, Hou Y L, Zhang S M and Shan J. 2019. Graph attention convolution for point cloud semantic segmentation//Proceedings of 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, CA, USA: IEEE: 10288-10297 [DOI: 10.1109/CVPR.2019.01054http://dx.doi.org/10.1109/CVPR.2019.01054]
Wang Y and Solomon J M. 2019. Deep closest point: learning representations for point cloud registration//Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. Seoul, Korea (South): IEEE: 3522-3531 [DOI: 10.1109/ICCV.2019.00362http://dx.doi.org/10.1109/ICCV.2019.00362]
Wu Z R, Song S R, Khosla A, Yu F, Zhang L G, Tang X O and Xiao J X. 2015. 3D shapenets: a deep representation for volumetric shapes///Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE: 1912-1920 [DOI: 10.1109/CVPR.2015.7298801http://dx.doi.org/10.1109/CVPR.2015.7298801]
Yang B S and Dong Z. 2019. Progress and perspective of point cloud intelligence. Acta Geodaetica et Cartographica Sinica, 48(12): 1575-1585
杨必胜, 董震. 2019. 点云智能研究进展与趋势. 测绘学报, 48(12): 1575-1585 [DOI: 10.11947/j.AGCS.2019.20190465http://dx.doi.org/10.11947/j.AGCS.2019.20190465]
Yang B S and Dong Z. 2020. Progress of point cloud intelligence，Beijing Science Press：1 （杨必胜, 董震. 2020. 点云智能处理. 北京: 科学出版社: 1）
Yang B S, Dong Z, Liang F X and Liu Y. 2016. Automatic registration of large-scale urban scene point clouds based on semantic feature points. ISPRS Journal of Photogrammetry and Remote Sensing, 113: 43-58 [DOI: 10.1016/j.isprsjprs.2015.12.005http://dx.doi.org/10.1016/j.isprsjprs.2015.12.005]
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.2014.10.005http://dx.doi.org/10.1016/j.isprsjprs.2014.10.005]
Yang B S and Zang Y F. 2014. Automated registration of dense terrestrial laser-scanning point clouds using curves. ISPRS Journal of Photogrammetry and Remote Sensing, 95: 109-121 [DOI: 10.1016/j.isprsjprs.2014.05.012http://dx.doi.org/10.1016/j.isprsjprs.2014.05.012]
Yew Z J and Lee G H. 2018. 3DFeat-Net: weakly supervised local 3D features for point cloud registration//Proceedings of the 15th European Conference on Computer Vision. Munich: Springer: 630-646 [DOI: 10.1007/978-3-030-01267-0_37http://dx.doi.org/10.1007/978-3-030-01267-0_37]
Yi L, Shao L, Savva M, Huang H B, Zhou Y, Wang Q R, Graham B, Engelcke M, Klokov R, Lempitsky V, Gan Y, Wang P Y, Liu K, Yu F G, Shui P P, Hu B Y, Zhang Y, Li Y Y, Bu R, Sun M C, Wu W, Jeong M, Choi J, Kim C, Geetchandra A, Murthy N, Ramu B, Manda M, Ramanathan M, Kumar G, Preetham P, Srivastava S, Bhugra S, Lall B, Haene C, Tulsiani S, Malik J, Lafer J, Jones R, Li
Yu Y T, Li J, Guan H Y, Wang C and Wen C L. 2016. Bag of contextual-visual words for road scene object detection from mobile laser scanning data. IEEE Transactions on Intelligent Transportation Systems, 17(12): 3391-3406 [DOI: 10.1109/TITS.2016.2550798http://dx.doi.org/10.1109/TITS.2016.2550798]
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