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Constructing digitial elevation model(DEM) from dense LiDAR points becomes increasingly important. Natural Neighbor Interpolation (NNI) is a popular approach to DEM construction from point datasets but is computationally intensive. In this study, we present a set of General Purpose computing Graphics Processing Unit(GPGPU) based algorithms that can significant speed up the process. Evaluating three real world LiDAR datasets each contains 6~7 million points shows that our CUDA baseddoi:10.1145/2345316.2345349 dblp:conf/comgeo/YouZ12 fatcat:s33ibpzdy5cizpj4swopp6wlpy