Natural neighbor interpolation based grid DEM construction using a GPU

With modern LiDAR technology the amount of topographic data, in the form of massive point clouds, has increased dramatically. One of the most fundamental GIS tasks is to construct a grid digital elevation model (DEM) from these 3D point clouds. In this paper we present a simple yet very fast algorithm for constructing a grid DEM from massive point clouds using natural neighbor interpolation (NNI). We use a graphics processing unit (GPU) to significantly speed up the computation. To handle the large data sets and to deal with graphics hardware limitations clever blocking schemes are used to partition the point cloud. For example, using standard desktop computers and graphics hardware, we construct a high-resolution grid with 150 million cells from two billion points in less than thirty-seven minutes. This is about one-tenth of the time required for the same computer to perform a standard linear interpolation, which produces a much less smooth surface.

[1]  Randima Fernando,et al.  The GeForce 6 series GPU architecture , 2005, SIGGRAPH Courses.

[2]  Jens H. Krüger,et al.  A Survey of General‐Purpose Computation on Graphics Hardware , 2007, Eurographics.

[3]  John Hart,et al.  ACM Transactions on Graphics: Editorial , 2003, SIGGRAPH 2003.

[4]  David Blythe.  The Direct3D 10 system , 2006, ACM Trans. Graph..

[5]  Eitan Grinspun,et al.  Sparse matrix solvers on the GPU: conjugate gradients and multigrid , 2003, SIGGRAPH Courses.

[6]  Thomas A. Hennig,et al.  The Shuttle Radar Topography Mission , 2001, Digital Earth Moving.

[7]  Pankaj K. Agarwal,et al.  I/O-Efficient Construction of Constrained Delaunay Triangulations , 2005, ESA.

[8]  Dinesh Manocha,et al.  Fast and approximate stream mining of quantiles and frequencies using graphics processors , 2005, SIGMOD '05.

[9]  Suresh Venkatasubramanian,et al.  Hardware-Assisted Natural Neighbor Interpolation , 2005, ALENEX/ANALCO.

[10]  Lars Arge.  External-Memory Algorithms with Applications in Geographic Information Systems , 1997 .

[11]  Herbert Edelsbrunner,et al.  Three-dimensional alpha shapes , 1992, VVS.

[12]  Pankaj K. Agarwal,et al.  From Point Cloud to Grid DEM: A Scalable Approach , 2006 .

[13]  Tom Davis,et al.  OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R), Version 2 (5th Edition) (OpenGL) , 2005 .

[14]  Dinesh Manocha,et al.  Fast and reliable collision detection using graphics processors , 2005, Symposium on Computational Geometry.

[15]  Dave Shreiner,et al.  OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R), Version 2.1 , 2007 .

[16]  H. Mitásová,et al.  Interpolation by regularized spline with tension: I. Theory and implementation , 1993 .

[17]  Henry P. Moreton,et al.  The GeForce 6800 , 2005, IEEE Micro.

[18]  Tom Davis,et al.  Opengl programming guide: the official guide to learning opengl , 1993 .

[19]  Nabil H. Mustafa,et al.  Streaming Geometric Optimization Using Graphics Hardware , 2003, ESA.

[20]  Guillaume Caumon,et al.  Concurrent number cruncher: a GPU implementation of a general sparse linear solver , 2009, Int. J. Parallel Emergent Distributed Syst..

[21]  R. Sibson,et al.  A brief description of natural neighbor interpolation , 1981 .

[22]  Arie E. Kaufman,et al.  GPU-Based flow simulation with complex boundaries , 2010 .

[23]  Pankaj K. Agarwal,et al.  TerraStream: from elevation data to watershed hierarchies , 2007, GIS.

[24]  Dinesh Manocha,et al.  Fast computation of generalized Voronoi diagrams using graphics hardware , 1999, SIGGRAPH.