{"id":4862,"date":"2011-07-23T22:11:18","date_gmt":"2011-07-23T19:11:18","guid":{"rendered":"http:\/\/hgpu.org\/?p=4862"},"modified":"2011-07-23T22:11:18","modified_gmt":"2011-07-23T19:11:18","slug":"importance-point-projection-for-gpu-based-final-gathering","status":"publish","type":"post","link":"https:\/\/hgpu.org\/?p=4862","title":{"rendered":"Importance Point Projection for GPU-based Final Gathering"},"content":{"rendered":"<p>We present a practical importance-driven method for GPU-based final gathering. We take as input a point cloud representing directly illuminated scene geometry; we then project and splat the points to microbuffers, which store each shading pixel&#8217;s occluded radiance field. We select points for projection based on importance, defined as each point&#8217;s estimated contribution to a shading pixel. For each selected point, we calculate its splat size adaptively based on its importance value. The main advantage of our method is that it&#8217;s simple and fast, and provides the capability to incorporate additional importance factors such as glossy reflection paths. We also introduce an image-space adaptive sampling method, which combines adaptive image subdivision with joint bilateral upsampling to robustly preserve fine details. We have implemented our algorithm on the GPU, providing high-quality rendering for dynamic scenes at near interactive rates.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present a practical importance-driven method for GPU-based final gathering. We take as input a point cloud representing directly illuminated scene geometry; we then project and splat the points to microbuffers, which store each shading pixel&#8217;s occluded radiance field. We select points for projection based on importance, defined as each point&#8217;s estimated contribution to a [&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":[180,11,89,3],"tags":[1797,1782,14,20,379,144],"class_list":["post-4862","post","type-post","status-publish","format-standard","hentry","category-3d-graphics-and-realism","category-computer-science","category-nvidia-cuda","category-paper","tag-3d-graphics-and-realism","tag-computer-science","tag-cuda","tag-nvidia","tag-nvidia-geforce-gtx-480","tag-rendering"],"views":2092,"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4862","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=4862"}],"version-history":[{"count":0,"href":"https:\/\/hgpu.org\/index.php?rest_route=\/wp\/v2\/posts\/4862\/revisions"}],"wp:attachment":[{"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4862"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4862"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/hgpu.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4862"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}