Software to convert terrestrial LiDAR scans of natural environments into photorealistic meshes

The introduction of 3D scanning has strongly influenced environmental sciences. If the resulting point clouds can be transformed into polygon meshes, a vast range of visualisation and analysis tools can be applied. But extracting accurate meshes from large point clouds gathered in natural environments is not trivial, requiring a suite of customisable processing steps. We present Habitat3D, an open source software tool to generate photorealistic meshes from registered point clouds of natural outdoor scenes. We demonstrate its capability by extracting meshes of different environments: 8,800m2 grassland featuring several Eucalyptus trees (combining 9 scans and 41,989,885 data points); 1,018m2 desert densely covered by vegetation (combining 56 scans and 192,223,621 data points); a well-structured garden; and a rough, volcanic surface. The resultant reconstructions accurately preserve all spatial features with millimetre accuracy whilst reducing the memory load by up to 98.5%. This enables rapid visualisation of the environments using off-the-shelf game engines and graphics hardware.

[1]  Philippe Gourbesville,et al.  Spatial Global Sensitivity Analysis of High Resolution classified topographic data use in 2D urban flood modelling , 2016, Environ. Model. Softw..

[2]  Warren B. Cohen,et al.  A review of the role of active remote sensing and data fusion for characterizing forest in wildlife habitat models , 2016 .

[3]  Philip Lewis,et al.  Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data , 2013, Remote. Sens..

[4]  Nico Blodow,et al.  Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..

[5]  Wolfgang Stürzl,et al.  Looking and homing: how displaced ants decide where to go , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[6]  Michael M. Kazhdan,et al.  Poisson surface reconstruction , 2006, SGP '06.

[7]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[8]  Pierfranco Costabile,et al.  Enhancing river model set-up for 2-D dynamic flood modelling , 2015, Environ. Model. Softw..

[9]  Christian Früh,et al.  An Automated Method for Large-Scale, Ground-Based City Model Acquisition , 2004, International Journal of Computer Vision.

[10]  Richard A. Fournier,et al.  An architectural model of trees to estimate forest structural attributes using terrestrial LiDAR , 2011, Environ. Model. Softw..

[11]  Dimitri Lague,et al.  3D Terrestrial LiDAR data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology , 2011, ArXiv.

[12]  J. Zeil,et al.  Mapping the navigational knowledge of individually foraging ants, Myrmecia croslandi , 2013, Proceedings of the Royal Society B: Biological Sciences.

[13]  D. Cohen-Or,et al.  Curve skeleton extraction from incomplete point cloud , 2009, SIGGRAPH 2009.

[14]  Martin Beland,et al.  A model for deriving voxel-level tree leaf area density estimates from ground-based LiDAR , 2014, Environ. Model. Softw..

[15]  W. Cohen,et al.  Lidar Remote Sensing for Ecosystem Studies , 2002 .

[16]  Georgios Papaioannou,et al.  Virtual Archaeologist: Assembling the Past , 2001, IEEE Computer Graphics and Applications.

[17]  Andrew Thomas Hudak,et al.  LiDAR Utility for Natural Resource Managers , 2009, Remote. Sens..

[18]  Philippe Santenoise,et al.  Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment , 2012 .

[19]  Alan H. Strahler,et al.  Study of bat flight behavior by combining thermal image analysis with a LiDAR forest reconstruction , 2013 .

[20]  Norbert Pfeifer,et al.  Structuring laser-scanned trees using 3D mathematical morphology , 2004 .

[21]  Wolfgang Stürzl,et al.  Three-dimensional models of natural environments and the mapping of navigational information , 2015, Journal of Comparative Physiology A.

[22]  J. Zeil,et al.  How Wasps Acquire and Use Views for Homing , 2016, Current Biology.

[23]  Richard A. Fournier,et al.  A fine-scale architectural model of trees to enhance LiDAR-derived measurements of forest canopy structure , 2012 .

[24]  Hui Xu,et al.  Knowledge and heuristic-based modeling of laser-scanned trees , 2007, TOGS.

[25]  Boleslo E. Romero,et al.  Analytical applications of fine-scale terrestrial lidar at the imperial Inca site of Caranqui, northern highland Ecuador , 2014 .

[26]  Hugues Hoppe,et al.  New quadric metric for simplifying meshes with appearance attributes , 1999, Proceedings Visualization '99 (Cat. No.99CB37067).

[27]  Hans-Gerd Maas,et al.  The suitability of airborne laser scanner data for automatic 3D object reconstruction , 2001 .

[28]  H. Spiecker,et al.  Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density , 2015 .

[29]  J. Léon,et al.  High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants , 2014 .

[30]  Katharine J. M. Dickinson,et al.  Assessing the ecological application of lasergrammetric techniques to measure fine-scale vegetation structure , 2008, Ecol. Informatics.

[31]  Peter Lindstrom,et al.  Out-of-core simplification of large polygonal models , 2000, SIGGRAPH.

[33]  Bernhard Rebele,et al.  ROBEX – COMPONENTS AND METHODS FOR THE PLANETARY EXPLORATIONDEMONSTRATION MISSION , 2015 .

[34]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[35]  F. M. Danson,et al.  Terrestrial Laser Scanning for Plot-Scale Forest Measurement , 2015, Current Forestry Reports.

[36]  Bernd Hamann,et al.  Enabling scientific workflows in virtual reality , 2006, VRCIA '06.

[37]  Daniel Cohen-Or,et al.  L1-medial skeleton of point cloud , 2013, ACM Trans. Graph..

[38]  A. Hudak,et al.  Mapping snags and understory shrubs for a LiDAR-based assessment of wildlife habitat suitability , 2009 .

[39]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[40]  Felix Morsdorf,et al.  Assessing forest structural and physiological information content of multi-spectral LiDAR waveforms by radiative transfer modelling , 2009 .

[41]  Peter Schröder,et al.  Interpolating Subdivision for meshes with arbitrary topology , 1996, SIGGRAPH.

[42]  N. Pfeifer,et al.  AUTOMATIC RECONSTRUCTION OF SINGLE TREES FROM TERRESTRIAL LASER SCANNER DATA , 2004 .

[43]  Matt Olson,et al.  Automatic reconstruction of tree skeletal structures from point clouds , 2010, ACM Trans. Graph..

[44]  Chengcui Zhang,et al.  A progressive morphological filter for removing nonground measurements from airborne LIDAR data , 2003, IEEE Trans. Geosci. Remote. Sens..

[45]  M. Flood,et al.  LiDAR remote sensing of forest structure , 2003 .

[46]  Ruisheng Wang,et al.  3D building modeling using images and LiDAR: a review , 2013 .

[47]  L. Vierling,et al.  Lidar: shedding new light on habitat characterization and modeling , 2008 .

[48]  Gregory P. Asner,et al.  Effects of Vegetation Structure on the Location of Lion Kill Sites in African Thicket , 2016, PloS one.

[49]  Björn Karlsson,et al.  Beyond the C++ Standard Library: An Introduction to Boost , 2005 .

[50]  Antony Galton,et al.  Efficient generation of simple polygons for characterizing the shape of a set of points in the plane , 2008, Pattern Recognit..

[51]  Stephen Lin,et al.  View-dependent displacement mapping , 2003, ACM Trans. Graph..

[52]  T. Rabbani,et al.  SEGMENTATION OF POINT CLOUDS USING SMOOTHNESS CONSTRAINT , 2006 .

[53]  Sergi Foix,et al.  Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: Analysis and comparison , 2014 .

[54]  J. Brasington,et al.  Leafless roughness of complex tree morphology using terrestrial lidar , 2009 .

[55]  Tony DeRose,et al.  Piecewise smooth surface reconstruction , 1994, SIGGRAPH.