{"id":680919,"date":"2020-07-31T01:19:52","date_gmt":"2020-07-31T08:19:52","guid":{"rendered":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/?post_type=msr-research-item&#038;p=680919"},"modified":"2021-06-03T00:32:39","modified_gmt":"2021-06-03T07:32:39","slug":"leveraging-multi-view-image-sets-for-unsupervised-intrinsic-image-decomposition-and-highlight-separation","status":"publish","type":"msr-research-item","link":"https:\/\/cm-edgetun.pages.dev\/en-us\/research\/publication\/leveraging-multi-view-image-sets-for-unsupervised-intrinsic-image-decomposition-and-highlight-separation\/","title":{"rendered":"Leveraging Multi-View Image Sets for Unsupervised Intrinsic Image Decomposition and Highlight Separation"},"content":{"rendered":"<p><span style=\"text-transform: none;text-indent: 0px;letter-spacing: normal;font-family: 'Noto Sans', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen-Sans, Ubuntu, Cantarell, 'Helvetica Neue', sans-serif;font-size: 14px;font-style: normal;font-weight: 400;float: none;background-color: #ffffff\">We present an unsupervised approach for factorizing object appearance into highlight, shading, and albedo layers, trained by multi-view real images. To do so, we construct a multi-view dataset by collecting numerous customer product photos online, which exhibit large illumination variations that make them suitable for training of reflectance separation and can facilitate object-level decomposition. The main contribution of our approach is a proposed image representation based on local color distributions that allows training to be insensitive to the local misalignments of multi-view images. In addition, we present a new guidance cue for unsupervised training that exploits synergy between highlight separation and intrinsic image decomposition. Over a broad range of objects, our technique is shown to yield state-of-the-art results for both of these tasks.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We present an unsupervised approach for factorizing object appearance into highlight, shading, and albedo layers, trained by multi-view real images. To do so, we construct a multi-view dataset by collecting numerous customer product photos online, which exhibit large illumination variations that make them suitable for training of reflectance separation and can facilitate object-level decomposition. The [&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":null,"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"AAAI Conference on Artificial Intelligence 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