{"id":464061,"date":"2018-01-31T18:32:51","date_gmt":"2018-02-01T02:32:51","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=464061"},"modified":"2019-05-10T19:05:14","modified_gmt":"2019-05-11T02:05:14","slug":"real-time-seamless-single-shot-6d-object-pose-prediction","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/real-time-seamless-single-shot-6d-object-pose-prediction\/","title":{"rendered":"Real-Time Seamless Single Shot 6D Object Pose Prediction"},"content":{"rendered":"

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [Kehl et al. 2017 (opens in new tab)<\/span><\/a>] that only predicts an approximate 6D pose that must then be re\ufb01ned, ours is accurate enough not to require additional post-processing. As a result, it is much faster \u2013 50 fps on a Titan X (Pascal) GPU \u2013 and more suitable for real-time processing. The key component of our method is a new CNN architecture inspired by YOLO [Redmon et al. 2016 (opens in new tab)<\/span><\/a>, Redmon and Farhadi 2017 (opens in new tab)<\/span><\/a>] that directly predicts the 2D image locations of the projected vertices of the object\u2019s 3D bounding box. The object\u2019s 6D pose is then estimated using a PnP algorithm. For single object and multiple object pose estimation on the LINEMOD and OCCLUSION datasets, our approach substantially outperforms other recent CNN-based approaches [Kehl et al. 2017 (opens in new tab)<\/span><\/a>, Rad and Lepetit 2017 (opens in new tab)<\/span><\/a>] when they are all used without post processing. During post-processing, a pose re\ufb01nement step can be used to boost the accuracy of these two methods, but at 10 fps or less, they are much slower than our method.<\/p>\n","protected":false},"excerpt":{"rendered":"

We propose a single-shot approach for simultaneously detecting an object in an RGB image and predicting its 6D pose without requiring multiple stages or having to examine multiple hypotheses. Unlike a recently proposed single-shot technique for this task [Kehl et al. 2017] that only predicts an approximate 6D pose that must then be re\ufb01ned, ours […]<\/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":"IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 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