{"id":4403,"date":"2011-06-20T10:19:21","date_gmt":"2011-06-20T10:19:21","guid":{"rendered":"http:\/\/hgpu.org\/?p=4403"},"modified":"2011-06-20T10:19:21","modified_gmt":"2011-06-20T10:19:21","slug":"distance-field-transform-with-an-adaptive-iteration-method","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4403","title":{"rendered":"Distance field transform with an adaptive iteration method"},"content":{"rendered":"<p>We propose a novel distance field transform method based on an iterative method adaptively performed on an evolving active band. Our method utilizes a narrow band to store active grid points being computed. Unlike the conventional fast marching method, we do not maintain a priority queue, and instead, perform iterative computing inside the band. This new algorithm alleviates the programming complexity and the data-structure (e.g. a heap) maintenance overhead, and leads to a parallel amenable computational process. During the active band propagating from a starting boundary layer, each grid point stays in the band for a lifespan time, which is determined by analyzing the particular geometric property of the grid structure. In this way, we find the Face-Centered Cubic (FCC) grid is a good 3D structure for distance transform.We further develop a multiple-segment method for the band propagation, achieving the computational complexity of O(m middot N) with a segment-related constant m.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose a novel distance field transform method based on an iterative method adaptively performed on an evolving active band. Our method utilizes a narrow band to store active grid points being computed. Unlike the conventional fast marching method, we do not maintain a priority queue, and instead, perform iterative computing inside the band. This [&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":[36,11,3],"tags":[1787,1782,135],"class_list":["post-4403","post","type-post","status-publish","format-standard","hentry","category-algorithms","category-computer-science","category-paper","tag-algorithms","tag-computer-science","tag-distance-transform"],"views":2346,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4403","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=4403"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4403\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4403"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4403"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4403"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}