In the ray-tracing community, the surface-area heuristic (SAH) is used as a de facto standard strategy for building high-quality kd-trees. Although widely accepted as the best kd-tree construction method, it is based only on the surface-area measure, which often fails to re ect effectively the rendering characteristics of a given scene. This paper presents new cost metrics that help produce improved kd-trees for static scenes by considering the visibility of geometric objects, which can affect significantly the actual distribution of rays during ray tracing. Instead of the SAH, we apply a different heuristic based on the new concept of voxel visibility, which allows more sophisticated estimation of the chance of a voxel being hit by rays. The first cost metric we present aims at constructing a single kd-tree that is used to trace both primary and secondary rays, whereas the second one is more relevant to secondary rays, involving re ection\/refraction or shadowing, whose distribution properties differ from those for primary rays. Our experiments, using both CPU-based and GPU-based computation with several test scenes, demonstrate that the presented cost metrics can reduce markedly the cost of ray-traversal computation and increase significantly the overall frame rate for ray tracing.<\/p>\n","protected":false},"excerpt":{"rendered":"
In the ray-tracing community, the surface-area heuristic (SAH) is used as a de facto standard strategy for building high-quality kd-trees. Although widely accepted as the best kd-tree construction method, it is based only on the surface-area measure, which often fails to re ect effectively the rendering characteristics of a given scene. This paper presents new […]<\/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":[11,89,3],"tags":[1782,14,412,20,379,181,144],"class_list":["post-7052","post","type-post","status-publish","format-standard","hentry","category-computer-science","category-nvidia-cuda","category-paper","tag-computer-science","tag-cuda","tag-kd-tree","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-raytracing","tag-rendering"],"views":2475,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7052","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=7052"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/7052\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7052"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7052"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7052"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}