In this paper, we introduce a parallel numerical scheme, the Lattice Boltzmann method, to shape modeling applications. The motivation of using this originally-designed fluid dynamics solver in surface modeling is its simplicity, locality, parallelism from the cellular-automata-originated updating rules, which can directly be mapped onto modern graphics hardware. A surface is implicitly represented by the signed distance field. The distances are then used in a modified LBM scheme as its computing primitive, instead of the densities in traditional LBM. The scheme can simulate curvature motions to smooth the surface with a diffusion process. Furthermore, an initial value level set method can be implemented for surface morphing. The distance difference between a morphing surface and a target surface defines the speed function of the evolving level sets, and is used as the driving force in the LBM. Our GPU-accelerated LBM algorithm has achieved outstanding performance for the denoising and morphing examples. It has the great potential to be further applied as a general GPU computing framework to many other solid and shape modeling applications.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we introduce a parallel numerical scheme, the Lattice Boltzmann method, to shape modeling applications. The motivation of using this originally-designed fluid dynamics solver in surface modeling is its simplicity, locality, parallelism from the cellular-automata-originated updating rules, which can directly be mapped onto modern graphics hardware. A surface is implicitly represented by the […]<\/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":[33,3],"tags":[841,1786,108,20,183,182],"class_list":["post-2742","post","type-post","status-publish","format-standard","hentry","category-image-processing","category-paper","tag-filtering","tag-image-processing","tag-lattice-boltzmann-model","tag-nvidia","tag-nvidia-geforce-8800-gtx","tag-opengl"],"views":1975,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2742","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=2742"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/2742\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2742"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2742"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2742"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}