We recently introduced an efficient technique for out-of-core rendering and management of large textured landscapes. The technique, called Batched Dynamic Adaptive Meshes (BDAM), is based on a paired tree structure: a tiled quadtree for texture data and a pair of bintrees of small triangular patches for the geometry. These small patches are TINs that are constructed and optimized off-line with high quality simplification and tristripping algorithms. Hierarchical view frustum culling and view-dependendent texture\/geometry refinement is performed at each frame with a stateless traversal algorithm that renders a continuous adaptive terrain surface by assembling out of core data. Thanks to the batched CPU\/GPU communication model, the proposed technique is not processor intensive and fully harnesses the power of current graphics hardware. This paper summarizes the method and discusses the results obtained in a virtual flythrough over a textured digital landscape derived from aerial imaging.<\/p>\n","protected":false},"excerpt":{"rendered":"
We recently introduced an efficient technique for out-of-core rendering and management of large textured landscapes. The technique, called Batched Dynamic Adaptive Meshes (BDAM), is based on a paired tree structure: a tiled quadtree for texture data and a pair of bintrees of small triangular patches for the geometry. These small patches are TINs that are […]<\/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":[11,3],"tags":[1782,20,144],"class_list":["post-1638","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-paper","tag-computer-science","tag-nvidia","tag-rendering"],"views":1880,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1638","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=1638"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/1638\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=1638"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=1638"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=1638"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}