Advanced Point Clouds Processing
Learning Goals:
Point clouds have become vital of importance spatial data source besides imagery, and vector map data, and are widely used in a variety of geospatial applications, such as geospatial intelligence, autonomous driving, smart cities, and so on. The serial of lectures will talk about the concepts, capture/generation methods of point clouds, the processing pipeline of point clouds (e.g., segmentation, classification), and latest progress in the key methods/algorithms of point clouds processing, and project examples of point clouds. The lesson consists of classroom lectures, field practice, and project practice. It will make you to learn how to use point clouds for 3D geospatial information extraction, 3D modeling, and geospatial applications.
Schedule:
Lecture 1: Concepts, capture/generation methods of point clouds
Lecture 2: Data format of point clouds, tools for 3D visualization of point clouds
Lecture 3: Segmentation, classification of point clouds
Lecture 4: Field practice: Terrestrial laser scanning tutorial for point cloud capture
Lecture 5: Lab practice
Lecture 6: Lab practice
Lecture 7: Group discussion
Lecture 8: Presentation& Project report
Reading list:
1. Zhen Dong, Bisheng Yang*,Pingbo Hu，Sebastian Scherer, 2018. An Efficient Global Energy Optimization Approach for Robust 3D Plane Segmentation of Point Clouds, ISPRS Journal of Photogrammetry and Remote Sensing 137, 112-133.
2. Jianping Li, Bisheng Yang*, Chi Chen, Ronggang Huang, Zhen Dong, Wen Xiao, 2018. Automatic registration of panoramic image sequence and mobile laser scanning data using semantic features,ISPRS Journal of Photogrammetry and Remote Sensing 136, 41-57
3. Bisheng Yang, Yuan Liu, Zhen Dong, Fuxun Liang, Bijun Li, Xiangyang Peng, 2017. 3D local feature BKD to extract road information from mobile laser scanning point clouds, ISPRS Journal of Photogrammetry and Remote Sensing, 130:329-343
4. Zhen Dong, Bisheng Yang, Yuan Liu, Fuxun Liang, Bijun Li, Yufu Zang, 2017. A novel binary shape context for 3D local surface description, ISPRS Journal of Photogrammetry and Remote Sensing, 130:431-452.
5. Bisheng Yang, Zhen Dong, Yuan Liu, Fuxun Liang, Yongjun Wang. 2017. Computing multiple aggregation levels and contextual features for road facilities recognition using mobile laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 126:180-194
6. Bisheng Yang, Ronggang Huang, Jianping Li, Mao Tian, Wenxia Dai, Ruofei Zhong, 2016. Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space, Remote Sensing, 9:14.
7. Bisheng Yang, Zhen Dong, Fuxun Liang, Yuan Liu, 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.
8. Bisheng Yang, Zhen Dong, Gang Zhao, Wenxia Dai, 2015. Hierarchical Extraction of Urban Objects from Mobile Laser Scanning Data, ISPRS Journal of Photogrammetry and Remote Sensing, 99:45-57.
9. Qiuping Li, Hongchao Fan, Xuechen Luan, Bisheng Yang, Lin Liu, 2014. Polygon-based
10. Haiyan Guan, Jonathan Li, Yongtao Yu, Cheng Wang, Michael Chapman, Bisheng Yang, 2014. Using mobile laser scanning data for automated extraction of road markings, ISPRS Journal of Photogrammetry and Remote Sensing, 87, 93-107.
11. Bisheng Yang, Zhen Dong, 2013. A shape-based segmentation method for mobile laser scanning point clouds, ISPRS Journal of Photogrammetry and Remote Sensing, 81:19-30.
12. Bisheng Yang, Lina Fang, Jonathan Li, 2013. Semi-automated Extraction and Delineation of 3D Roads of Street Scene from Mobile Laser Scanning Point Clouds, ISPRS Journal of Photogrammetry and Remote Sensing,79:80-93.
13. Bisheng Yang, Zhen Wei, Qingquan Li, Jonathan Li, 2012, Automated Extraction of Street-scene Objects from Mobile Lidar Point Clouds. International Journal of Remote Sensing, 33(18):5839-5861.
14. Bisheng Yang, Lina Fang, Qingquan Li, Jonathan Li, 2012, Automated Extraction of Road Markings from Mobile Lidar Point Clouds. Photogrammetric Engineering & Remote Sensing, 78(4):331-338.
15. Yu Y., Li J., Wen C., et al., 2016. Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data. ISPRS Journal of Photogrammetry and Remote Sensing, 113: 106-123.
16. Vosselman, G., Gorte, B.G.H., Sithole, G., Rabbani, T., 2004. Recognising structure in laser scanner point clouds. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 46, part 8/W2, Freiburg, Germany, October 4-6, pp. 33-38.
17. Pu, S., Rutzingerc, M., Vosselmand, G., Oude Elberinkd, S., 2011. Recognizing basic structures from mobile laser scanning data for road inventory studies. ISPRS Journal of Photogrammetry and Remote Sensing 66(6), S28-S39.
18. Barnea, S., Filin, S., 2013. Segmentation of terrestrial laser scanning data using geometry and image information. ISPRS J. Photogramm. Remote Sens. 76, 33–48.
