{"id":171371,"date":"2014-06-30T05:34:52","date_gmt":"2014-06-30T05:34:52","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/project\/sparse-reflections-analysis\/"},"modified":"2017-06-16T17:26:23","modified_gmt":"2017-06-17T00:26:23","slug":"sparse-reflections-analysis","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/sparse-reflections-analysis\/","title":{"rendered":"Sparse Reflections Analysis"},"content":{"rendered":"
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We present Sparse Reflections Analysis (SRA), an algorithm for removing multipath interference from Time of Flight sensors. SRA allows for very general forms of multipath, including interference with three or more paths, diffuse multipath resulting from Lambertian surfaces, and combinations thereof. SRA removes this general multipath with robust techniques based on L1 optimization. Further, due to a novel dimension reduction, we present a very fast version of SRA, which can run at frame rate.<\/div>\n","protected":false},"excerpt":{"rendered":"

We present Sparse Reflections Analysis (SRA), an algorithm for removing multipath interference from Time of Flight sensors. SRA allows for very general forms of multipath, including interference with three or more paths, diffuse multipath resulting from Lambertian surfaces, and combinations thereof. SRA removes this general multipath with robust techniques based on L1 optimization. Further, due […]<\/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":""},"research-area":[13561,13562,13552],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-171371","msr-project","type-msr-project","status-publish","hentry","msr-research-area-algorithms","msr-research-area-computer-vision","msr-research-area-hardware-devices","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"2014-06-30","related-publications":[243260],"related-downloads":[234999],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","value":"eyalk","display_name":"Eyal Krupka","author_link":"Eyal Krupka<\/a>","is_active":false,"user_id":31771,"last_first":"Krupka, Eyal","people_section":0,"alias":"eyalk"}],"msr_research_lab":[],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171371"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":2,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171371\/revisions"}],"predecessor-version":[{"id":391235,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171371\/revisions\/391235"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=171371"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=171371"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=171371"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=171371"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=171371"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}