References

Contents

References#

Citation#

To cite PDAL in publications use:

PDAL Contributors, 2024. PDAL Point Data Abstraction Library. https://doi.org/10.5281/zenodo.10884408

A BibTeX entry for LaTeX users is

@misc{pdal_contributors_2024_2616780,

author = {PDAL Contributors},

title = {PDAL Point Data Abstraction Library},

month = aug,

year = 2024,

doi = {10.5281/zenodo.10884408},

url = {

https://doi.org/10.5281/zenodo.10884408

}

}

A paper about PDAL by the team, “PDAL: An open source library for the processing and analysis of point clouds”, is available at [Butler et al., 2021].

Reference#

[1]

ALoopingIcon. Meshing point clouds. \emph MESHLAB STUFF, September 2009. Accessed: 2015-11-13. URL: http://meshlabstuff.blogspot.com/2009/09/meshing-point-clouds.html.

[2]

Marc Bartels and Hong Wei. Threshold-free object and ground point separation in lidar data. Pattern recognition letters, 31(10):1089–1099, 2010.

[3]

Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. Lof: identifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data, 93–104. 2000.

[4]

Howard Butler, Bradley Chambers, Preston Hartzell, and Craig Glennie. PDAL: an open source library for the processing and analysis of point clouds. Computers & Geosciences, 148:104680, 2021.

[5]

Ziyue Chen, Bernard Devereux, Bingbo Gao, and Gabriel Amable. Upward-fusion urban dtm generating method using airborne lidar data. ISPRS journal of photogrammetry and remote sensing, 72:121–130, 2012.

[6]

Robert L Cook. Stochastic sampling in computer graphics. ACM Transactions on Graphics (TOG), 5(1):51–72, 1986.

[7]

Jérôme Demantké, Clément Mallet, Nicolas David, and Bruno Vallet. Dimensionality based scale selection in 3d lidar point clouds. In Laserscanning. 2011.

[8]

Mark AZ Dippé and Erling Henry Wold. Antialiasing through stochastic sampling. In Proceedings of the 12th annual conference on Computer graphics and interactive techniques, 69–78. 1985.

[9]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu, and others. A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd, volume 96, 226–231. 1996.

[10]

Kaspar Fischer, Bernd Gärtner, and Martin Kutz. Fast smallest-enclosing-ball computation in high dimensions. In European Symposium on Algorithms, 630–641. Springer, 2003.

[11]

D. Gatziolis and R. J. McGaughey. Reconstructing aircraft trajectories from multi-return airborne laser-scanning data. Remote Sensing, 2019.

[12]

Craig L. Glennie. Rigorous 3D error analysis of kinematic scanning LIDAR systems. Journal of Applied Geodesy, jan 2007.

[13]

Stéphane Guinard and Loic Landrieu. Weakly supervised segmentation-aided classification of urban scenes from 3d lidar point clouds. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42:151–157, 2017.

[14]

Michael Kazhdan, Matthew Bolitho, and Hugues Hoppe. Poisson surface reconstruction. In Proceedings of the fourth Eurographics symposium on Geometry processing, volume 7. 2006.

[15]

Wenkai Li, Qinghua Guo, Marek K Jakubowski, and Maggi Kelly. A new method for segmenting individual trees from the lidar point cloud. Photogrammetric Engineering & Remote Sensing, 78(1):75–84, 2012.

[16]

Xuechen Li, Yinlong Liu, Yiru Wang, Chen Wang, Manning Wang, and Zhijian Song. Fast and Globally Optimal Rigid Registration of 3D Point Sets by Transformation Decomposition. unknown, 2019. Available at https://arxiv.org/pdf/1812.11307.pdf.

[17]

Frederico A Limberger and Manuel M Oliveira. Real-time detection of planar regions in unorganized point clouds. Pattern Recognition, 48(6):2043–2053, 2015.

[18]

Stuart Lloyd. Least squares quantization in pcm. IEEE transactions on information theory, 28(2):129–137, 1982.

[19]

Michael McCool and Eugene Fiume. Hierarchical poisson disk sampling distributions. In Graphics interface, volume 92, 94–105. 1992.

[20]

Andriy Myronenko and Xubo Song. Point set registration: coherent point drift. IEEE transactions on pattern analysis and machine intelligence, 32(12):2262–75, dec 2010.

[21]

Thomas J Pingel, Keith C Clarke, and William A McBride. An improved simple morphological filter for the terrain classification of airborne lidar data. ISPRS journal of photogrammetry and remote sensing, 77:21–30, 2013.

[22]

Radu Bogdan Rusu, Zoltan Csaba Marton, Nico Blodow, Mihai Dolha, and Michael Beetz. Towards 3d point cloud based object maps for household environments. Robotics and Autonomous Systems, 56(11):927–941, 2008.

[23]

Tim Weyrich, Mark Pauly, Richard Keiser, Simon Heinzle, Sascha Scandella, and Markus Gross. Post-processing of scanned 3d surface data. In First Eurographics conference on Point-Based Graphics. Eurographics Association, 2004.

[24]

Heng Yang, Jingnan Shi, and Luca Carlone. Teaser: fast and certifiable point cloud registration. IEEE Transactions on Robotics, 37(2):314–333, 2020.

[25]

Alan L. Yuille and Norberto M. Grzywacz. The Motion Coherence Theory. Second International Conference on Computer Vision, 1988.

[26]

Keqi Zhang, Shu-Ching Chen, Dean Whitman, Mei-Ling Shyu, Jianhua Yan, and Chengcui Zhang. A progressive morphological filter for removing nonground measurements from airborne lidar data. IEEE transactions on geoscience and remote sensing, 41(4):872–882, 2003.

[27]

Wuming Zhang, Jianbo Qi, Peng Wan, Hongtao Wang, Donghui Xie, Xiaoyan Wang, and Guangjian Yan. An easy-to-use airborne lidar data filtering method based on cloth simulation. Remote sensing, 8(6):501, 2016.

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