<\/h1>\n<\/h2>\nOverview<\/h2>\n
Overview<\/h2>\n
All scenes were recorded from a handheld Kinect RGB-D camera at 640×480 resolution. We use an implementation of the KinectFusion<\/a> system to obtain the ‘ground truth’ camera tracks, and a dense 3D model. Several sequences were recorded per scene by different users, and split into distinct training and testing sequence sets. Details on how this data can be used for example for the evaluation of relocalization methods can be found in our papers listed under publications.<\/p>\n <\/span><\/span><\/p>\n For each scene, we provide one zip file which contains several sequences. Each sequence is a continuous stream of tracked RGB-D camera frames. Tracking\u00a0has been performed using ICP and frame-to-model alignment\u00a0with respect to a dense reconstruction\u00a0represented by a\u00a0truncated signed distance\u00a0volume.<\/p>\n Each sequence (seq-XX.zip)\u00a0consists of 500-1000 frames. Each frame consists of three files:<\/p>\n For each scene, we further provide:<\/p>\n Please note:<\/strong> The RGB and depth camera have not been calibrated and we can’t provide calibration parameters at the moment. The recorded frames correspond to the raw, uncalibrated camera images. In the KinectFusion pipeline we used the following default intrinsics for the depth camera: Principle point (320,240), Focal length (585,585).<\/p>\n The data is provided for non-commercial use only. By downloading the data, you\u00a0accept\u00a0the license agreement<\/a> which can be downloaded here<\/a>.<\/p>\n If you report results based on the 7-scenes dataset, please cite at least one of the papers mentioned under publications. You may choose the paper that is more relevant to your own publication.<\/p>\n The 7-Scenes dataset is a collection of tracked RGB-D camera frames. The dataset may be used for evaluation of methods for different applications such as dense tracking and mapping and relocalization techniques. Overview All scenes were recorded from a handheld Kinect RGB-D camera at 640×480 resolution. We use an implementation of the KinectFusion system to […]<\/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":[13556],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-171165","msr-project","type-msr-project","status-publish","hentry","msr-research-area-artificial-intelligence","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"2013-01-01","related-publications":[164357,165138],"related-downloads":[],"related-videos":[],"related-groups":[],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Antonio Criminisi","user_id":41790,"people_section":"Section name 0","alias":"acriminisi"}],"msr_research_lab":[],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171165"}],"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":4,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171165\/revisions"}],"predecessor-version":[{"id":875994,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171165\/revisions\/875994"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=171165"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=171165"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=171165"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=171165"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=171165"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Data Description<\/h2>\n
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License Agreement<\/h2>\n
Citations<\/h2>\n
Downloads<\/h2>\n
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Chess<\/a>, Fire<\/a>, Heads<\/a>, Office<\/a>, Pumpkin<\/a>, RedKitchen<\/a>, Stairs<\/a><\/h5>\n<\/li>\n
All TSDF Volumes<\/a><\/h5>\n<\/li>\n
License Agreement<\/a><\/h5>\n<\/li>\n<\/ul>\n<\/div>\n","protected":false},"excerpt":{"rendered":"