Visibility analysis for the occlusion detection and characterisation in street point clouds acquired with Mobile Laser Scanning

References

• Balado J, Arias P, Lorenzo H, Meijide-Rodríguez A. 2021. Disturbance analysis in the classification of objects obtained from urban LiDAR point clouds with convolutional neural networks. Remote Sens. 13(11):2135.
   Google Scholar
• Balado J, Díaz-Vilariño L, Arias P, González-Jorge H. 2018. Automatic classification of urban ground elements from mobile laser scanning data. Autom Constr. 86:226–239.
   Web of Science ®Google Scholar
• Balado J, Díaz-Vilariño L, Arias P, Lorenzo H. 2019. Point clouds for direct pedestrian pathfinding in urban environments. ISPRS J Photogramm Remote Sens. 148:184–196.
   Web of Science ®Google Scholar
• Balado J, Díaz-Vilariño L, Arias P, Soilán M. 2017. Automatic building accessibility diagnosis from point clouds. Autom Constr. 82:103–111.
   Web of Science ®Google Scholar
• Balado J, González E, Verbree E, Díaz-Vilariño L, Lorenzo H, Balado J. 2020. Automatic detection and characterization of ground occlusions in urban point clouds from Mobile Laser Scanning data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci. 6.
   Google Scholar
• Bauwens S, Bartholomeus H, Calders K, Lejeune P. 2016. Forest inventory with terrestrial LiDAR: a comparison of static and hand-held mobile laser scanning. Forests. 7(6):127.
   Web of Science ®Google Scholar
• Boulch A, Saux BL, Audebert N. 2017. Unstructured point cloud semantic labeling using deep segmentation networks. Proceedings of the Eurographics Workshop on 3D Object Retrieval [Internet]. Goslar Germany, Germany: Eurographics Association. p. 17–24..
   Google Scholar
• Buyssens P, Daisy M, Tschumperle D, Lezoray O. 2015. Exemplar-based inpainting: technical review and new heuristics for better geometric reconstructions. IEEE Trans Image Process. 24:1809–1824.
   PubMed Web of Science ®Google Scholar
• Charles RQ, Su H, Kaichun M, Guibas LJ. 2017. PointNet: deep learning on point sets for 3D classification and segmentation. 2017 IEEE Conference on Computer Vision and Pattern Recognition. p. 77–85.
   Google Scholar
• Che E, Jung J, Olsen MJ. 2019. Object recognition, segmentation, and classification of mobile laser scanning point clouds: a state of the art review. Sensors. 19(4):810. https://www.mdpi.com/1424-8220/19/4/810.
   PubMed Web of Science ®Google Scholar
• Chen C, Yang B. 2016. Dynamic occlusion detection and inpainting of in situ captured terrestrial laser scanning point clouds sequence. ISPRS J Photogramm Remote Sens. 119:90–107. http://www.sciencedirect.com/science/article/pii/S0924271616300922.
   Web of Science ®Google Scholar
• Chen J, Yi JS, Kahoush M, Cho ES, Cho YK. 2020. Point cloud scene completion of obstructed building facades with generative adversarial inpainting. Sensors. 20(18):5029.
   PubMed Web of Science ®Google Scholar
• De Blasiis MR, Di Benedetto A, Fiani M. 2020. Mobile laser scanning data for the evaluation of pavement surface distress. Remote Sens. 12(6):942. https://www.mdpi.com/2072-4292/12/6/942.
   Google Scholar
• Doria D, Radke RJ. 2012. Filling large holes in LiDAR data by inpainting depth gradients. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Work. p. 65–72.
   Google Scholar
• Han X, Dong Z, Yang B. 2021. A point-based deep learning network for semantic segmentation of MLS point clouds. ISPRS J Photogramm Remote Sens. 175:199–214. https://www.sciencedirect.com/science/article/pii/S0924271621000642.
   Web of Science ®Google Scholar
• Huang P, Cheng M, Chen Y, Luo H, Wang C, Li J. 2017. Traffic sign occlusion detection using mobile laser scanning point clouds. IEEE Trans Intell Transport Syst. 18(9):2364–2376.
   Web of Science ®Google Scholar
• Huang Z, Yu Y, Xu J, Ni F, Le X. 2020. PF-Net: point fractal network for 3D point cloud completion. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
   Google Scholar
• Huang T, Zou H, Cui J, Yang X, Wang M, Zhao X, Zhang J, Yuan Y, Xu Y, Liu Y. 2021. RFNet: recurrent forward network for dense point cloud completion. Proceedings of the IEEE/CVF International Conference on Computer Vision. p. 12508–12517.
   Google Scholar
• Kroemer O, Amor HB, Ewerton M, Peters J. 2012. Point cloud completion using extrusions. 2012 12th IEEE-RAS International Conference on Humanoid Robot (Humanoids 2012). p. 680–685.
   Google Scholar
• Ku T, Veltkamp RC, Boom B, Duque-Arias D, Velasco-Forero S, Deschaud J-E, Goulette F, Marcotegui B, Ortega S, Trujillo A, et al. 2020. SHREC 2020: 3D point cloud semantic segmentation for street scenes. Comput Graph. 93:13–24. http://www.sciencedirect.com/science/article/pii/S0097849320301400.
   Web of Science ®Google Scholar
• Kukko A, Kaartinen H, Hyyppä J, Chen Y. 2012. Multiplatform mobile laser scanning: usability and performance. Sensors. 12(9):11712–11733.
   Web of Science ®Google Scholar
• Kunyuan C, Ming C, Menglan Z, Xinqu C, Yifei C, Jonathan L, Hongshan N. 2015. Automated object extraction from MLS data: a survey. 2015 10th International Conference on Intelligent Systems and Knowledge Engineering. p. 331–334.
   Google Scholar
• Landrieu L, Simonovsky M. 2018. Large-scale point cloud semantic segmentation with superpoint graphs. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. p. 4558–4567.
   Google Scholar
• Lawin FJ, Danelljan M, Tosteberg P, Bhat G, Khan FS, Felsberg M. 2017. Deep projective 3D semantic segmentation. In: Felsberg M, Heyden A, Krüger N, editors. Computer analysis of images and patterns. Cham: Springer International Publishing. p. 95–107.
   Google Scholar
• Li F, Lehtomäki M, Oude Elberink S, Vosselman G, Kukko A, Puttonen E, Chen Y, Hyyppä J. 2019. Semantic segmentation of road furniture in mobile laser scanning data. ISPRS J Photogramm Remote Sens. 154:98–113. https://www.sciencedirect.com/science/article/pii/S092427161930142X.
   Web of Science ®Google Scholar
• Li Z, Liqiang Z, Mathiopoulos PT, Liu F, Liang Z, Li S, Liu H. 2017. A hierarchical methodology for urban facade parsing from TLS point clouds. ISPRS J Photogramm Remote Sens. 123:75–93. https://www.sciencedirect.com/science/article/pii/S0924271616305408.
   Web of Science ®Google Scholar
• Lian Y, Feng T, Zhou J. 2019. A dense Pointnet++ architecture for 3D point cloud semantic segmentation. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium. p. 5061–5064.
   Google Scholar
• Luo H, Chen C, Fang L, Khoshelham K, Shen G. 2020a. MS-RRFSegNet: multiscale regional relation feature segmentation network for semantic segmentation of urban scene point clouds. IEEE Trans Geosci Remote Sens. 58(12):8301–8315.
   Web of Science ®Google Scholar
• Luo H, Khoshelham K, Fang L, Chen C. 2020b. Unsupervised scene adaptation for semantic segmentation of urban mobile laser scanning point clouds. ISPRS J Photogramm Remote Sens. 169:253–267. https://www.sciencedirect.com/science/article/pii/S0924271620302744.
   Web of Science ®Google Scholar
• Mohd Noor N, Ibrahim I, Abdullah A, Abdullah AA. 2020. Information fusion for cultural heritage three-dimensional modeling of Malay cities. ISPRS Int J Geo-Information. 9(3).
   PubMed Web of Science ®Google Scholar
• Qin R, Gruen A. 2014. 3D change detection at street level using mobile laser scanning point clouds and terrestrial images. ISPRS J Photogramm Remote Sens. 90:23–35. https://www.sciencedirect.com/science/article/pii/S0924271614000100.
   Web of Science ®Google Scholar
• Rastiveis H, Shams A, Sarasua WA, Li J. 2020. Automated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference. ISPRS J Photogramm Remote Sens. 160:149–166. https://www.sciencedirect.com/science/article/pii/S0924271619302977.
   Web of Science ®Google Scholar
• Salamanca S, Merchán P, Hernández E, Adan A, Cerrada C. 2008. Filling holes in 3D meshes using image restoration algorithms. 4th International Symposium on 3D Data Processing, Visualization and Transmission, 3DPVT 2008.
   Google Scholar
• Serna A, Marcotegui B. 2013. Urban accessibility diagnosis from mobile laser scanning data. ISPRS J Photogramm Remote Sens. 84:23–32.
   Web of Science ®Google Scholar
• Serna A, Marcotegui B. 2014. Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning. ISPRS J Photogramm Remote Sens. 93:243–255.
   Web of Science ®Google Scholar
• Soilán M, Riveiro B, Martínez-Sánchez J, Arias P. 2017. Segmentation and classification of road markings using MLS data. ISPRS J Photogramm Remote Sens. 123:94–103. http://linkinghub.elsevier.com/retrieve/pii/S0924271616303173.
   Web of Science ®Google Scholar
• Soilán M, Riveiro B, Sánchez-Rodríguez A, Arias P. 2018. Safety assessment on pedestrian crossing environments using MLS data. Accid Anal Prev. 111:328–337. http://www.sciencedirect.com/science/article/pii/S0001457517304475.
   PubMed Web of Science ®Google Scholar
• Sun Z, Xu Y, Hoegner L, Stilla U. 2018. Classification of MLS point clouds in urban scenes using detrended geometric features from supervoxel-based local contexts. ISPRS Ann Photogramm Remote Sens Spat Inf Sci. IV-2:271–278. https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2/271/2018/.
   Google Scholar
• Tan W, Qin N, Ma L, Li Y, Du J, Cai G, Yang K, Li J. 2020. Toronto-3D: a large-scale mobile LiDAR dataset for semantic segmentation of urban roadways. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Work [Internet]. http://dx.doi.org/10.1109/CVPRW50498.2020.00109.
   Google Scholar
• Tchapmi L, Choy C, Armeni I, Gwak J, Savarese S. 2017. SEGCloud: semantic segmentation of 3D point clouds. 2017 International Conference on 3D Vision. p. 537–547.
   Google Scholar
• Vallet B, Brédif M, Serna A, Marcotegui B, Paparoditis N. 2015. TerraMobilita/iQmulus urban point cloud analysis benchmark. Comput Graph. 49:126–133. http://dx.doi.org/10.1016/j.cag.2015.03.004.
   Web of Science ®Google Scholar
• Wang X, Jr, Marcelo HA, Lee GH. 2020. Cascaded refinement network for point cloud completion. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
   Google Scholar
• Wang Y, Chen Q, Zhu Q, Liu L, Li C, Zheng D. 2019. A survey of mobile laser scanning applications and key techniques over urban areas. Remote Sens. 11(13):1543.
   Google Scholar
• Wen X, Xiang P, Han Z, Cao Y-P, Wan P, Zheng W, Liu Y-S. 2021. PMP-Net: point cloud completion by learning multi-step point moving paths. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. p. 7443–7452.
   Google Scholar
• Xia S, Wang R. 2019. Façade separation in ground-based LiDAR point clouds based on edges and Windows. IEEE J Sel Top Appl Earth Observ Remote Sens. 12(3):1041–1052.
   Web of Science ®Google Scholar
• Xiao W, Vallet B, Brédif M, Paparoditis N. 2015. Street environment change detection from mobile laser scanning point clouds. ISPRS J Photogramm Remote Sens. 107:38–49. http://www.sciencedirect.com/science/article/pii/S0924271615001215.
   Web of Science ®Google Scholar
• Xie C, Wang C, Zhang B, Yang H, Chen D, Wen F. 2021. Style-based point generator with adversarial rendering for point cloud completion. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. p. 4619–4628.
   Google Scholar
• Yang B, Dong Z, Zhao G, Dai W. 2015. Hierarchical extraction of urban objects from mobile laser scanning data. ISPRS J Photogramm Remote Sens. 99:45–57. http://www.sciencedirect.com/science/article/pii/S092427161400255X.
   Web of Science ®Google Scholar
• Ye X, Li J, Huang H, Du L, Zhang X. 2018. 3D recurrent neural networks with context fusion for point cloud semantic segmentation. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y, editors. Computer vision – ECCV 2018. Cham: Springer International Publishing. p. 415–430.
   Google Scholar
• Yu X, Rao Y, Wang Z, Liu Z, Lu J, Zhou J. 2021. PoinTr: diverse point cloud completion with geometry-aware transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision. p. 12498–12507.
   Google Scholar
• Yuan W, Khot T, Held D, Mertz C, Hebert M. 2018. PCN: point completion network. 2018 International Conference on 3D Vision. p. 728–737.
   Google Scholar
• Zhang X, Feng Y, Li S, Zou C, Wan H, Zhao X, Guo Y, Gao Y. 2021. View-guided point cloud completion. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. p. 15890–15899.
   Google Scholar
• Zhong M, Sui L, Wang Z, Hu D. 2020a. Pavement crack detection from mobile laser scanning point clouds using a time grid. Sensors. 20(15):4198.
   PubMed Web of Science ®Google Scholar
• Zhong M, Sui L, Wang Z, Yang X, Zhang C, Chen N. 2020b. Recovering missing trajectory data for mobile laser scanning systems. Remote Sens. 12(6):899.
   Google Scholar
• Zhu J, Gehrung J, Huang R, Borgmann B, Sun Z, Hoegner L, Hebel M, Xu Y, Stilla U. 2020. TUM-MLS-2016: an annotated mobile LiDAR dataset of the TUM city campus for semantic point cloud interpretation in urban areas. Remote Sens. 12(11):1875.
   Google Scholar