References
- Akenine-Möller, T. and Eric Haines, N.H., 2008. Real-time rendering. New York: A.K. Peters Ltd.
- Al-Awami, A.K., et al., 2014. Neurolines - a subway map metaphor for visualizing nanoscale neuronal connectivity. IEEE Transactions on Visualization and Computer Graphics, 20 (12), 2369–2378. doi:10.1109/TVCG.2014.2346312
- Arrowsmith, J. and Zielke, O., 2009. Tectonic geomorphology of the San Andreas fault zone from high resolution topography: an example from the Cholame segment. Geomorphology, 113 (1–2), 70–81. doi:10.1016/j.geomorph.2009.01.002
- Boucheny, C., 2009. Visualisation scientifique de grands volumes de données: pour une approche perceptive. Theses. Université Joseph-Fourier - Grenoble I.
- Brunori, C.A., et al., 2013. Characterization of active fault scarps from LiDAR data: a case study from Central Apennines (Italy). International Journal of Geographical Information Science, 27 (7), 1405–1416. doi:10.1080/13658816.2012.684385
- Comino, M., et al., 2017. Error-aware construction and rendering of multi-scan panoramas from massive point clouds. Computer Vision and Image Understanding, 157, 43–54. Large-Scale 3D Modeling of Urban Indoor or Outdoor Scenes from Images and Range Scans. doi:10.1016/j.cviu.2016.09.011.
- Debattista, K., et al., 2017. Frame rate vs resolution: a subjective evaluation of spatiotemporal perceived quality under varying computational budgets. Computer Graphics Forum, 37 (1), 363–374.
- Deibe, D., et al., 2017. GVLiDAR: an interactive web-based visualization framework to support geospatial measures on Lidar data. International Journal of Remote Sensing., 38 (3), 827–849. doi:10.1080/01431161.2016.1271476
- Discher, S., Richter, R., and Döllner, J., 2017. Interactive and view-dependent see-through lenses for massive 3d point clouds. In: A. Abdul-Rahman, ed. Advances in 3D Geoinformation. New York City: Springer International Publishing, 49–62.
- Gao, Z., et al., 2014. Visualizing aerial Lidar cities with hierarchical hybrid point-polygon structures. In: Proceedings of Graphics Interface 2014, GI ‘14. Canadian Information Processing Society, Montréal, QC, 137–144.
- Gobbetti, E. and Marton, F., 2004. Layered point clouds: a simple and efficient multiresolution structure for distributing and rendering gigantic point-sampled models. Computers & Graphics, 28 (6), 815–826. doi:10.1016/j.cag.2004.08.010
- González-Ferreiro, E., et al., 2013. Modelling stand biomass fractions in Galician eucalyptus globulus plantations by use of different Lidar pulse densities. Forest Systems, 22 (3), 510–525. doi:10.5424/fs/2013223-03878
- Goswami, P., et al., 2013. An efficient multi-resolution framework for high quality interactive rendering of massive point clouds using multi-way kd-trees. The Visual Computer, 29 (1), 69–83. doi:10.1007/s00371-012-0675-2
- Gross, M. and Pfister, H., 2007. Point-based graphics. Burlington, MA: Morgan Kaufmann.
- Isenburg, M., 2015. LASzip: lossless compression of LiDAR data.
- Kereszturi, G., et al., 2012. LiDAR based quantification of lava flow susceptibility in the City of Auckland (New Zealand). Remote Sensing of Environment, 125, 198–213. doi:10.1016/j.rse.2012.07.015
- Kovač, B. and Žalik, B., 2010. Visualization of LiDAR datasets using point-based rendering technique. Computers and Geosciences, 36 (11), 1443–1450. doi:10.1016/j.cageo.2010.02.011
- Kuder, M. and Žalik, B., 2011. Web-based LiDAR visualization with point-based rendering. In: 2011 Seventh International Conference on Signal-Image Technology and Internet-Based Systems (SITIS), Dijon, France, 38–45.
- Lafarge, F. and Mallet, C., 2012. Creating large-scale city models from 3D point clouds: a robust approach with hybrid representation. International Journal of Computer Vision, 99 (1), 69–85. doi:10.1007/s11263-012-0517-8
- Mongus, D. and Žalik, B., 2011. Efficient method for lossless LiDAR data compression. International Journal of Remote Sensing, 32 (9), 2507–2518. doi:10.1080/01431161003698385
- Passalacqua, P., Tarolli, P., and Foufoula-Georgiou, E., 2010. Testing space-scale methodologies for automatic geomorphic feature extraction from LiDAR in a complex mountainous landscape. Water Resources Research, 46 (11), 1–17. doi:10.1029/2009WR008812
- Richter, R., Discher, S., and Döllner, J., 2015. Out-of-core visualization of classified 3D point clouds. In: M. Breunig, et al., eds. 3d geoinformation science: the selected papers of the 3d geoinfo 2014. New York City: Springer International Publishing, 227–242.
- Rodríguez, M.B., et al., 2013. Coarse-grained multiresolution structures for mobile exploration of gigantic surface models. In: SIGGRAPH Asia 2013 Symposium on Mobile Graphics and Interactive Applications, SA ’13, Hong Kong. ACM, 1–6.
- Roering, J.J., et al., 2013. You are here: connecting the dots with airborne LiDAR for geomorphic fieldwork. Geomorphology, 200, 172–183. doi:10.1016/j.geomorph.2013.04.009
- Sallenger, A.H., et al., 1999. Airborne laser study quantifies el niño-induced coastal change. Eos, Transactions American Geophysical Union, 80 (8), 89–92. doi:10.1029/99EO00056
- Tarolli, P., 2014. High-resolution topography for understanding earth surface processes: opportunities and challenges. Geomorphology, 216, 295–312. doi:10.1016/j.geomorph.2014.03.008
- Ventura, G., et al., 2011. Tracking and evolution of complex active landslides by multi-temporal airborne LiDAR data: the Montaguto landslide (Southern Italy). Remote Sensing of Environment, 115 (12), 3237–3248. doi:10.1016/j.rse.2011.07.007
- 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 (Supplement C), 295–310. doi:10.1016/j.rse.2014.11.001
- Yuan, S., et al., 2017. Feature preserving multiresolution subdivision and simplification of point clouds: a conformal geometric algebra approach. Mathematical Methods in the Applied Sciences, 41(11), 4074–4087.