{"id":155094,"date":"2020-02-20T11:36:04","date_gmt":"2004-03-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/sampling-the-disparity-space-image\/"},"modified":"2020-11-03T18:27:11","modified_gmt":"2020-11-04T02:27:11","slug":"sampling-the-disparity-space-image","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/sampling-the-disparity-space-image\/","title":{"rendered":"Sampling the disparity space image"},"content":{"rendered":"<div class=\"asset-content\">\n<p>A central issue in stereo algorithm design is the choice of matching cost. Many algorithms simply use squared or absolute intensity differences based on integer disparity steps. In this paper, we address potential problems with such approaches. We begin with a careful analysis of the properties of the continuous disparity space image (DSI) and propose several new matching cost variants based on symmetrically matching interpolated image signals. Using stereo images with ground truth, we empirically evaluate the performance of the different cost variants and show that proper sampling can yield improved matching performance.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A central issue in stereo algorithm design is the choice of matching cost. Many algorithms simply use squared or absolute intensity differences based on integer disparity steps. In this paper, we address potential problems with such approaches. We begin with a careful analysis of the properties of the continuous disparity space image (DSI) and propose [&hellip;]<\/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":"","msr-author-ordering":[{"type":"user_nicename","value":"szeliski"}],"msr_publishername":"IEEE Computer Society","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"IEEE Transactions on Pattern Analysis and Machine Intelligence","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"IEEE Transactions on Pattern Analysis and Machine Intelligence","msr_number":"3","msr_organization":"","msr_pages_string":"419-425","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"26","msr_copyright":"Copyright \u00a9 2007 IEEE.   Reprinted from IEEE Computer Society.\r\n\r\nThis material is posted here with permission of the IEEE.  Internal or personal use of this material is permitted.  However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org.\r\n\r\nBy choosing to view this document, you agree to all provisions of the copyright laws protecting 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