Ray-tracing is one of the most researched areas in 3D photo-realistic image synthesis. We propose the T-BUFFER algorithm, which is a modification to the conventional ray-tracing algorithm and compares very favorably with it in terms of performance, which is achieved primarily through reduction in the number of unsuccessful intersection tests. Our major contribution is the integration of a shadow-buffering technique which, in conjunction with the t-buffer, can create ray-traced renderings with reasonably accurate shadows much more efficiently than the common approach, while giving the user control over the quality of shadows and the related computational efficiency. We support the above claims with empirical evidence which suggest an approximately linear gain over the original ray-tracing algorithm.<\/p>\n
We also describe REALITY – a complete rendering engine which has been developed for the purpose of empirical comparison between the T-BUFFER algorithm and the classical ray-tracing algorithm. Both algorithms were implemented in a common code base to facilitate direct comparison. The engine also implements the above mentioned shadow-buffering technique to accelerate shadow calculations. We present a thorough comparative analysis of the original algorithm and the T-BUFFER algorithm, based on the rendering times of REALITY and show that the results strongly corroborate with the linear gain expected theoretically.<\/p>\n","protected":false},"excerpt":{"rendered":"
Ray-tracing is one of the most researched areas in 3D photo-realistic image synthesis. We propose the T-BUFFER algorithm, which is a modification to the conventional ray-tracing algorithm and compares very favorably with it in terms of performance, which is achieved primarily through reduction in the number of unsuccessful intersection tests. Our major contribution is the […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13561,13551],"msr-publication-type":[193715],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-309293","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-algorithms","msr-research-area-graphics-and-multimedia","msr-locale-en_us"],"msr_publishername":"","msr_edition":"","msr_affiliation":"","msr_published_date":"2004-7-22","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"International Journal of Information Technology","msr_volume":"10","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"309296","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/10\/14.pdf","id":"309296","title":"14","label_id":"243132","label":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"Nikunj Raghuvanshi","user_id":33106,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=Nikunj Raghuvanshi"},{"type":"text","value":"Sanjay G. 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