{"id":7560,"date":"2012-05-10T23:36:31","date_gmt":"2012-05-10T20:36:31","guid":{"rendered":"http:\/\/hgpu.org\/?p=7560"},"modified":"2012-05-10T23:36:31","modified_gmt":"2012-05-10T20:36:31","slug":"constructing-natural-neighbor-interpolation-based-grid-dem-using-cuda","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=7560","title":{"rendered":"Constructing Natural Neighbor Interpolation Based Grid DEM Using CUDA"},"content":{"rendered":"<p>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 based implementation on a NVIDIA GTX 480 GPU card is 1-2 orders faster than the current state-of-the-art.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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 [&hellip;]<\/p>\n","protected":false},"author":351,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[36,11,89,3],"tags":[1787,1782,14,667,20,379,609],"class_list":["post-7560","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-nvidia-cuda","category-paper","tag-algorithms","tag-computer-science","tag-cuda","tag-databases","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-software-engineering"],"views":2345,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7560","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/users\/351"}],"replies":[{"embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7560"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7560\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7560"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7560"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7560"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}