The DOI matches its Crossref record; 33 of the 34 references it cites were found in a registry or an index.
Incremental and Enhanced Scanline-Based Segmentation Method for Surface Reconstruction of Sparse LiDAR Data
Open accessAbstract
The segmentation of point clouds is an important aspect of automated processing tasks such as semantic extraction. However, the sparsity and non-uniformity of the point clouds gathered by the popular 3D mobile LiDAR devices pose many challenges for existing segmentation methods. To improve the segmentation results of point clouds from mobile LiDAR devices, we propose an optimized segmentation method based on Scanline Continuity Constraint (SLCC) in this work. Unlike conventional scanline-based segmentation methods, SLCC clusters scanlines using the continuity constraints in terms of the distance as well as the direction of two consecutive points. In addition, scanline clusters are agglomerated not only into primitive geometrical shapes but also irregular shapes. Another downside to existing segmentation methods is that they are not capable of incremental processing. This causes unnecessary memory and time consumption for applications that require frame-wise segmentation or when new point clouds are added. In order to address this, we propose an incremental scheme—the Incremental Recursive Segmentation (IRIS), that can be easily applied to any segmentation method. IRIS is achieved by combining the segments of newly added point clouds and the previously segmented results. Furthermore, as an example application, we construct a processing pipeline consisting of plane fitting and surface reconstruction using the segmentation results. Finally, we evaluate the proposed methods on th…
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“For lidar, several filtering methods can be used to reduce clutter in the data such as ground extraction [162] or map-based filtering [163].”
From A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2020) · cites this paper as [162]
Citations
Cites
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3D is here: Point Cloud Library (PCL)
Rusu, R., & Cousins, S. (2011) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980567 · Cited by 5,513
LOAM: Lidar Odometry and Mapping in Real-time
Zhang, J., & Singh, S. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.007 · Cited by 3,303
Iterative point matching for registration of free-form curves and surfaces
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Efficient RANSAC for Point‐Cloud Shape Detection
Schnabel, R., Wahl, R., & Klein, R. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01016.x · Cited by 2,222
3D Semantic Parsing of Large-Scale Indoor Spaces
Armeni, I., Şener, O., Zamir, A., Jiang, H., Brilakis, I., Fischer, M. A., & Savarese, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.170 · Cited by 1,897
On the shape of a set of points in the plane
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Research works printed but not linked: 3
- Rabbani, 2006, Segmentation of point clouds using smoothness constraint, ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 36, 248
- Oehler, B., Stueckler, J., Welle, J., Schulz, D., and Behnke, S. (2011). Intelligent Robotics and Applications, Springer.
- Segal, A., Haehnel, D., and Thrun, S. (July, January 28). Generalized-ICP. Proceedings of the Robotics: Science and Systems V, Seattle, WA, USA.
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Sorted by citations
A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
“Traditional methods often used euclidean clustering [158] or region-growing methods [159] for grouping points into objects.”
Path planning algorithms in the autonomous driving system: A comprehensive review
Reda, M., Onsy, A., Haikal, A. Y., & Ghanbari, A. (2024) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2024.104630 · Cited by 348
Performance Analysis of 10 Models of 3D LiDARs for Automated Driving
Lambert, J., Carballo, A., Monrroy, A., Narksri, P., Wong, D., Takeuchi, E., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.3009680 · Cited by 118
Wang, W., Sakurada, K., & Kawaguchi, N. (2017) · Remote Sensing · doi:10.3390/rs9080851 · Cited by 116
Self-citationSemantic line framework-based indoor building modeling using backpacked laser scanning point cloud
Wang, C., Hou, S., Wen, C., Gong, Z., Li, Q., Sun, X., & Li, J. (2018) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2018.03.025 · Cited by 102
Metadata
- Authors
- Wang, W., Sakurada, K., & Kawaguchi, N.
- Affiliations
- Nagoya University (JP)
- Institutes of Innovation for Future Society, Nagoya University (JP)
- Corresponding author
- Wang, Weimin
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2016-11-22
- Volume
- 8
- Issue
- 11
- Pages
- 967
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2552088091
- Cited by
- 16
- References
- 34
- Citations by year
- 2026: 1 · 2024: 3 · 2023: 2 · 2021: 3 · 2020: 2 · 2019: 2 · 2018: 2 · 2017: 1
- FWCI
- 1.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Remote Sensing (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Life Sciences › Agricultural and Biological Sciences › Forestry › Remote Sensing and LiDAR Applications
- Keywords
- point cloud segmentation, mobile laser scanning, sparse point clouds, recursive segmentation, surface reconstruction, plane fitting, 3D point clouds
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