References
- Chen, Y., et al., 2022. DGCNN network architecture with densely connected point pairs in multiscale local regions for ALS point cloud classification. IEEE Geoscience and remote sensing letters, 19, 1–5.
- Chen, C., et al., 2023. Segmentation-based hierarchical interpolation filter using both geometric and radiometric features for LiDAR point clouds over complex scenarios. Measurement, 211, 112668.
- Hu, Q., et al. 2019. RandLA-Net: efficient semantic segmentation of large-scale point clouds. arXiv:1911.11236.
- Huang, R., Xu, Y., and Stilla, U., 2021. GraNet: Global relation-aware attentional network for semantic segmentation of ALS point clouds. ISPRS Journal of photogrammetry and remote sensing, 177, 1–20.
- Jiang, M., et al. 2018. PointSIFT: a SIFT-like network module for 3D point cloud semantic segmentation. In Computer vision and pattern recognition computer science – computer vision and pattern recognition. arXiv preprint arXiv:1807.00652.
- Jing, H., et al., 2021. Efficient point cloud corrections for mobile monitoring applications using road/rail-side infrastructure. Survey review – directorate of overseas surveys, 53 (378), 235–251.
- Li, Y., et al., 2018. PointCNN: convolution on X-transformed points. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS'18), New York, USA, 820–830.
- Luo, R., et al., 2022. 3D deformation monitoring method for temporary structures based on multi-thread LiDAR point cloud. Measurement, 200, 111545.
- Ni, H., Lin, X., and Zhang, J., 2017. Classification of ALS point cloud with improved point cloud segmentation and random forests. Remote sensing, 9 (3), 288.
- Niemeyer, J., Rottensteiner, F., and Soergel, U., 2014. Contextual classification of lidar data and building object detection in urban areas. ISPRS Journal of photogrammetry and remote sensing, 87, 152–165.
- Park, H., and Lee, D., 2019. Comparison between point cloud and mesh models using images from an unmanned aerial vehicle. Measurement, 138, 461–466.
- Qi, C.R., et al., 2017a. PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE conference on computervision and pattern recognition, Honolulu, HI, USA, 652–660.
- Qi, C.R., et al., 2017b. PointNet++: deep hierarchical feature learning on point sets in a metric space. In: proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS'17), Long Beach, California, USA, 5099–5108.
- Qin, N., Hu, X., and Dai, H., 2018. Deep fusion of multi-view and multimodal representation of ALS point cloud for 3D terrain scene recognition. ISPRS Journal of photogrammetry and remote sensing, 143, 205–212.
- Shapovalov, R., Velizhev, A., and Barinova, O., 2010. NON-associative Markov networks for 3d point cloud classification. In: Proceedings of the ISPRS Commission III symposium - PCV 2010, Saint-Mandé, France, 103–108.
- Shi, S., et al., 2020. PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection,In:2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 10526–10535.
- Wang, L., et al., 2019. Graph attention convolution for point cloud semantic segmentation. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), 2019-1-1, Long Beach, CA, USA, 10288–10297.
- Wang, Z., and Zhu, D., 2019. An accurate detection method for surface defects of complex components based on support vector machine and spreading algorithm. Measurement, 147, 106886.
- Weinmann, M., et al., 2015. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS Journal of photogrammetry and remote sensing, 105, 286–304.
- Weinmann, M., Jutzi, B., and Mallet, C., 2013. Feature relevance assessment for the semantic interpretation of 3D point cloud data. ISPRS Annals of the photogrammetry, remote sensing and spatial information sciences, 2, 313–318.
- Winiwarter, L., et al., 2019. Classification of ALS point clouds using end-to-end deep learning. PFG – Journal of photogrammetry, remote sensing and geoinformation science, 87 (3), 75–90.
- Wu, Z., et al. 2014. 3D ShapeNets: a deep representation for volumetric shapes, 1912–1920.
- Xu, S., Vosselman, G., and Elberink, S.O., 2014. Multiple-entity based classification of airborne laser scanning data in urban areas. ISPRS Journal of photogrammetry and remote sensing, 88, 1–15.
- Yakar, M., et al., 2023. Discontinuity set extraction from 3D point clouds obtained by UAV photogrammetry in a rockfall site. Survey review, 55 (392), 1–13.
- Yan, W.Y., Shaker, A., and El-Ashmawy, N., 2015. Urban land cover classification using airborne LiDAR data: a review. Remote sensing of environment, 158, 295–310.
- Yang, B., et al., 2017. Automated reconstruction of building LoDs from airborne LiDAR point clouds using an improved morphological scale space. Remote sensing, 9 (1), 1–23.
- Yang, Y., et al., 2020. Three-dimensional point cloud data subtle feature extraction algorithm for laser scanning measurement of large-scale irregular surface in reverse engineering. Measurement, 151, 107220.
- Yousefhussien, M., et al., 2018. A multi-scale fully convolutional network for semantic labeling of 3D point clouds. ISPRS Journal of photogrammetry and remote sensing, 143, 191–204.
- Zhang, L.L., et al., 2022. A deep learning based method for railway overhead wire reconstruction from airborne LiDAR data. Remote sensing, 14 (20), 1–23.
- Zhao, H., et al., 2019. PointWeb: enhancing local neighborhood features for point cloud processing. In: 2019 IEEE/CVF conference on computer vision and pattern recognition (CVPR), Long Beach, CA, USA, 5560–5568.
- Zhao, R., Pang, M., and Wang, J., 2018. Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network. International journal of geographical information science: IJGIS, 32 (5), 960–979.