This paper analyzed the efficient architecture features of massive terrain LOD visualization, and found that CPU can hardly select tiles from mass terrain effectively. This restricted the expansion of terrain’s size. Yacine Amara presented Tile Load Map(TLM). This paper presented Multilevel Tile Load Map (MTLM) algorithm for tile selection to extend TLM. MTLM uses 2d texture for saving tile quadtree (TQT), and executes tile view-frustum culling and level-of-detail selection by GPU acceleration. The scalability constraint was avoided by dealing tile selection with high-performance image processing in TLM. In addition, optimization was presented at tile data organization and resource management. tile data were reorganized by designing Pi-space filling curve to avoid locality of data access. Texture objects were managed by utilizing resource pool to reduce bandwidth. Experiments show that MTLM algorithm reduces the time of tile selection in terrain rendering, accelerates frame rate, and resolves the terrain scalability bottlenecks.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper analyzed the efficient architecture features of massive terrain LOD visualization, and found that CPU can hardly select tiles from mass terrain effectively. This restricted the expansion of terrain’s size. Yacine Amara presented Tile Load Map(TLM). This paper presented Multilevel Tile Load Map (MTLM) algorithm for tile selection to extend TLM. MTLM uses 2d […]<\/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":[36,11,33,3],"tags":[1787,1782,187,1786,20,1264,182,298,144,134],"class_list":["post-6759","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-image-processing","category-paper","tag-algorithms","tag-computer-science","tag-glsl","tag-image-processing","tag-nvidia","tag-nvidia-geforce-gt-240-m","tag-opengl","tag-optimization","tag-rendering","tag-visualization"],"views":2652,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6759","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=6759"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/6759\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=6759"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=6759"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=6759"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}