@article{bartels2010threshold,
  title={Threshold-free object and ground point separation in LIDAR data},
  author={Bartels, Marc and Wei, Hong},
  journal={Pattern recognition letters},
  volume={31},
  number={10},
  pages={1089--1099},
  year={2010},
  publisher={Elsevier}
}

@inproceedings{breunig2000lof,
  title={LOF: identifying density-based local outliers},
  author={Breunig, Markus M and Kriegel, Hans-Peter and Ng, Raymond T and Sander, J{\"o}rg},
  booktitle={Proceedings of the 2000 ACM SIGMOD international conference on Management of data},
  pages={93--104},
  year={2000}
}

@article{butler2021pdal,
  title={{PDAL}: An open source library for the processing and analysis of point clouds},
  author={Butler, Howard and Chambers, Bradley and Hartzell, Preston and Glennie, Craig},
  journal={Computers \& Geosciences},
  volume={148},
  pages={104680},
  year={2021},
  publisher={Elsevier}
}

@article{chen2012upward,
  title={Upward-fusion urban DTM generating method using airborne Lidar data},
  author={Chen, Ziyue and Devereux, Bernard and Gao, Bingbo and Amable, Gabriel},
  journal={ISPRS journal of photogrammetry and remote sensing},
  volume={72},
  pages={121--130},
  year={2012},
  publisher={Elsevier}
}

@article{cook1986stochastic,
  title={Stochastic sampling in computer graphics},
  author={Cook, Robert L},
  journal={ACM Transactions on Graphics (TOG)},
  volume={5},
  number={1},
  pages={51--72},
  year={1986},
  publisher={ACM New York, NY, USA}
}

@inproceedings{demantke2011dimensionality,
  title={Dimensionality based scale selection in 3D lidar point clouds},
  author={Demantk{\'e}, J{\'e}r{\^o}me and Mallet, Cl{\'e}ment and David, Nicolas and Vallet, Bruno},
  booktitle={Laserscanning},
  year={2011}
}

@inproceedings{dippe1985antialiasing,
  title={Antialiasing through stochastic sampling},
  author={Dipp{\'e}, Mark AZ and Wold, Erling Henry},
  booktitle={Proceedings of the 12th annual conference on Computer graphics and interactive techniques},
  pages={69--78},
  year={1985}
}

@inproceedings{ester1996density,
  title={A density-based algorithm for discovering clusters in large spatial databases with noise},
  author={Ester, Martin and Kriegel, Hans-Peter and Sander, J{\"o}rg and Xu, Xiaowei and others},
  booktitle={kdd},
  volume={96},
  number={34},
  pages={226--231},
  year={1996}
}

@inproceedings{fischer2003fast,
  title={Fast smallest-enclosing-ball computation in high dimensions},
  author={Fischer, Kaspar and G{\"a}rtner, Bernd and Kutz, Martin},
  booktitle={European Symposium on Algorithms},
  pages={630--641},
  year={2003},
  organization={Springer}
}

@article{guinard2017weakly,
  title={Weakly supervised segmentation-aided classification of urban scenes from 3D LiDAR point clouds},
  author={Guinard, St{\'e}phane and Landrieu, Loic},
  journal={The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume={42},
  pages={151--157},
  year={2017},
  publisher={Copernicus GmbH}
}

@inproceedings{kazhdan2006poisson,
  title={Poisson surface reconstruction},
  author={Kazhdan, Michael and Bolitho, Matthew and Hoppe, Hugues},
  booktitle={Proceedings of the fourth Eurographics symposium on Geometry processing},
  volume={7},
  number={4},
  year={2006}
}

@article{li2012new,
  title={A new method for segmenting individual trees from the lidar point cloud},
  author={Li, Wenkai and Guo, Qinghua and Jakubowski, Marek K and Kelly, Maggi},
  journal={Photogrammetric Engineering \& Remote Sensing},
  volume={78},
  number={1},
  pages={75--84},
  year={2012},
  publisher={American Society for Photogrammetry and Remote Sensing}
}

@article{limberger2015real,
  title={Real-time detection of planar regions in unorganized point clouds},
  author={Limberger, Frederico A and Oliveira, Manuel M},
  journal={Pattern Recognition},
  volume={48},
  number={6},
  pages={2043--2053},
  year={2015},
  publisher={Elsevier}
}

@article{lloyd1982least,
  title={Least squares quantization in PCM},
  author={Lloyd, Stuart},
  journal={IEEE transactions on information theory},
  volume={28},
  number={2},
  pages={129--137},
  year={1982},
  publisher={IEEE}
}

@inproceedings{mccool1992hierarchical,
  title={Hierarchical Poisson disk sampling distributions},
  author={McCool, Michael and Fiume, Eugene},
  booktitle={Graphics interface},
  volume={92},
  number={1},
  pages={94--105},
  year={1992}
}

@article{pingel2013improved,
  title={An improved simple morphological filter for the terrain classification of airborne LIDAR data},
  author={Pingel, Thomas J and Clarke, Keith C and McBride, William A},
  journal={ISPRS journal of photogrammetry and remote sensing},
  volume={77},
  pages={21--30},
  year={2013},
  publisher={Elsevier}
}

@article{rusu2008towards,
  title={Towards 3D point cloud based object maps for household environments},
  author={Rusu, Radu Bogdan and Marton, Zoltan Csaba and Blodow, Nico and Dolha, Mihai and Beetz, Michael},
  journal={Robotics and Autonomous Systems},
  volume={56},
  number={11},
  pages={927--941},
  year={2008},
  publisher={Elsevier}
}

@inproceedings{weyrich2004post,
  title={Post-processing of scanned 3D surface data},
  author={Weyrich, Tim and Pauly, Mark and Keiser, Richard and Heinzle, Simon and Scandella, Sascha and Gross, Markus},
  booktitle={First Eurographics conference on Point-Based Graphics},
  year={2004},
  organization={Eurographics Association}
}

@article{yang2020teaser,
  title={Teaser: Fast and certifiable point cloud registration},
  author={Yang, Heng and Shi, Jingnan and Carlone, Luca},
  journal={IEEE Transactions on Robotics},
  volume={37},
  number={2},
  pages={314--333},
  year={2020},
  publisher={IEEE}
}

@article{zhang2003progressive,
  title={A progressive morphological filter for removing nonground measurements from airborne LIDAR data},
  author={Zhang, Keqi and Chen, Shu-Ching and Whitman, Dean and Shyu, Mei-Ling and Yan, Jianhua and Zhang, Chengcui},
  journal={IEEE transactions on geoscience and remote sensing},
  volume={41},
  number={4},
  pages={872--882},
  year={2003},
  publisher={IEEE}
}

@article{zhang2016easy,
  title={An easy-to-use airborne LiDAR data filtering method based on cloth simulation},
  author={Zhang, Wuming and Qi, Jianbo and Wan, Peng and Wang, Hongtao and Xie, Donghui and Wang, Xiaoyan and Yan, Guangjian},
  journal={Remote sensing},
  volume={8},
  number={6},
  pages={501},
  year={2016},
  publisher={MDPI}
}

@misc{cite_mesh2009,
  author       = {ALoopingIcon},
  title        = {Meshing Point Clouds},
  howpublished = {\emph{MESHLAB STUFF}},
  month        = sep,
  year         = {2009},
  url          = {http://meshlabstuff.blogspot.com/2009/09/meshing-point-clouds.html},
  note         = {Accessed: 2015-11-13},
}

@article{Myronenko,
  abstract = {Point set registration is a key component in many computer vision tasks. The goal of point set registration is to assign correspondences between two sets of points and to recover the transformation that maps one point set to the other. Multiple factors, including an unknown nonrigid spatial transformation, large dimensionality of point set, noise, and outliers, make the point set registration a challenging problem. We introduce a probabilistic method, called the Coherent Point Drift (CPD) algorithm, for both rigid and nonrigid point set registration. We consider the alignment of two point sets as a probability density estimation problem. We fit the Gaussian mixture model (GMM) centroids (representing the first point set) to the data (the second point set) by maximizing the likelihood. We force the GMM centroids to move coherently as a group to preserve the topological structure of the point sets. In the rigid case, we impose the coherence constraint by reparameterization of GMM centroid locations with rigid parameters and derive a closed form solution of the maximization step of the EM algorithm in arbitrary dimensions. In the nonrigid case, we impose the coherence constraint by regularizing the displacement field and using the variational calculus to derive the optimal transformation. We also introduce a fast algorithm that reduces the method computation complexity to linear. We test the CPD algorithm for both rigid and nonrigid transformations in the presence of noise, outliers, and missing points, where CPD shows accurate results and outperforms current state-of-the-art methods.},
  author = {Myronenko, Andriy and Song, Xubo},
  issn = {1939-3539},
  journal = {IEEE transactions on pattern analysis and machine intelligence},
  month = {dec},
  number = {12},
  pages = {2262--75},
  pmid = {20975122},
  publisher = {Institute of Electrical {\&} Electronics Engineers (IEEE)},
  title = {{Point set registration: coherent point drift.}},
  volume = {32},
  year = {2010}
}

@article{Yuille1998,
  author = {Yuille, Alan L. and Grzywacz, Norberto M.},
  journal = {Second International Conference on Computer Vision},
  title = {{The Motion Coherence Theory}},
  year = {1988}
}

@article{Gatziolis2019,
  author = {D. Gatziolis and R. J. McGaughey},
  journal = {Remote Sensing},
  title = {{Reconstructing aircraft trajectories from multi-return airborne laser-scanning data}},
  volume = {11},
  number = {19},
  year = {2019}
}

@article{Xuechen2019,
  abstract = {The rigid registration of two 3D point sets is a fundamental problem in
  computer vision. The current trend is to solve this problem globally using the
  BnB optimization framework. However, the existing global methods are slow for
  two main reasons: the computational complexity of BnB is exponential to the
  problem dimensionality (which is six for 3D rigid registration), and the bound
  evaluation used in BnB is inefficient. In this paper, we propose two
  techniques to address these problems. First, we introduce the idea of
  translation invariant vectors, which allows us to decompose the search of a 6D
  rigid transformation into a search of 3D rotation followed by a search of 3D
  translation, each of which is solved by a separate BnB algorithm. This
  transformation decomposition reduces the problem dimensionality of BnB
  algorithms and substantially improves its efficiency. Then, we propose a new
  data structure, named 3D Integral Volume, to accelerate the bound evaluation
  in both BnB algorithms. By combining these two techniques, we implement an
  efficient algorithm for rigid registration of 3D point sets. Extensive
  experiments on both synthetic and real data show that the proposed algorithm
  is three orders of magnitude faster than the existing state-of-the-art global
  methods.},
  author = {Xuechen Li and Yinlong Liu and Yiru Wang and Chen Wang and Manning Wang and Zhijian Song},
  title = {{Fast and Globally Optimal Rigid Registration of 3D Point Sets by Transformation Decomposition}},
  year = {2019},
  journal = {unknown},
  note = {Available at \url{https://arxiv.org/pdf/1812.11307.pdf}}
}

@article{Glennie2007,
  author = {Glennie, Craig L.},
  journal = {Journal of Applied Geodesy},
  month = {jan},
  number = {3},
  publisher = {Walter de Gruyter GmbH},
  title = {{Rigorous 3D error analysis of kinematic scanning LIDAR systems}},
  volume = {1},
  year = {2007}
}

@article{lin2018supervoxel,
  author = {Yangbin Lin and Cheng Wang and Dawei Zhai and Wei Li and Jonathan Li},
  title = {Toward better boundary preserved supervoxel segmentation for 3D point clouds},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  volume = {143},
  pages = {39--47},
  year = {2018},
  month = {9},
  issn = {0924-2716},
  doi = {10.1016/j.isprsjprs.2018.05.004}
}
