{"id":285590,"date":"2016-08-30T20:04:07","date_gmt":"2016-08-31T03:04:07","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=285590"},"modified":"2017-05-31T11:17:04","modified_gmt":"2017-05-31T18:17:04","slug":"3d-object-reconstruction-recognition","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/3d-object-reconstruction-recognition\/","title":{"rendered":"3D Object Reconstruction and Recognition"},"content":{"rendered":"
\n

We study the problem of 3D object reconstruction and recognition. For reconstruction, we aim at developing algorithms and systems to lower down the barrier of 3D reconstruction for common users. In this way, we can collect a world-class 3D object repository via leveraging crowdsourcing. For recognition, we aim at dealing with a large-scale task (e.g. identifying thousands of objects), and providing real-time performance.<\/p>\n<\/div>\n

<\/div>\n
\n

1. 3D Object Reconstruction<\/h2>\n

coming soon …<\/p>\n

 <\/p>\n

2. 3D Object Recognition<\/h2>\n

\"3d_obj_recognition\"<\/p>\n

3D model-based object recognition has been a noticeable research trend in recent years. Common methods identify 2D-to-3D correspondences and make recognition decisions by RANSAC-based pose estimation, whose efficiency usually suffers from inaccurate correspondences caused by the increasing number of target objects for recognition. To overcome this scalability bottleneck, in this paper we propose an efficient 2D-to-3D correspondence filtering approach which combines a light-weight neighborhood-based step with a finer-grained pairwise step to remove spurious
\ncorrespondences by 2D\/3D geometric cues. On a dataset of 300 3D objects, the proposed solution achieves \uff5e10 times speed improvement over the baseline, with a comparable recognition accuracy. A non-GPU implementation achieves a speed of \uff5e3fps for 1280\u00d7720 query images.<\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

We study the problem of 3D object reconstruction and recognition. For reconstruction, we aim at developing algorithms and systems to lower down the barrier of 3D reconstruction for common users. In this way, we can collect a world-class 3D object repository via leveraging crowdsourcing. For recognition, we aim at dealing with a large-scale task (e.g. […]<\/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":[13551],"msr-locale":[268875],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-285590","msr-project","type-msr-project","status-publish","hentry","msr-research-area-graphics-and-multimedia","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"2015-05-01","related-publications":[],"related-downloads":[],"related-videos":[],"related-groups":[144916],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[{"id":0,"name":"Dataset","content":"

MSR-Object3D-300 Dataset<\/h2>\r\n\"3dobj_recognition_demo\"\r\n\r\nThis dataset contains multi-view images for 300 3D objects that are rigid and sufficiently textured. Each 3D object has ~250 images (1280 by 720 pixels) captured from different viewpoints with a clean background, using a turntable and a webcam (with a fixed focal length 1000 pixels). The dataset can be used for research on 3D reconstruction and object recognition.\r\n

Format<\/h3>\r\nThe data for each 3D object is provided in a separate folder, which contains:\r\n1) a \"db_img\" subfolder for the multi-view images of the object;\r\n2) a \"list_db_img.txt\" file that lists the filenames of the images;\r\n3) a \"model.nvm\" file containing an example 3D point cloud model reconstructed from the images using VisualSFM toolkit (http:\/\/homes.cs.washington.edu\/~ccwu\/vsfm\/<\/a>). The model file (after copied to the image subfolder) can be opened with VisualSFM to show a possible 3D representation of the object. You can freely reconstruct other 3D models by yourself.\r\n

Download<\/h3>\r\nSince the size of this dataset is large (~23GB), we partition it into 12 parts, each of which (<2GB) can be downloaded and decompressed separately from:\r\nhttp:\/\/sdrv.ms\/11FJxys<\/a>\r\nOR\r\nhttps:\/\/skydrive.live.com\/redir?resid=C13A8BE5BADFFECA!196&authkey=!AHXAPlcJhDMWSNo<\/a>\r\n

Reference<\/h3>\r\nWe would appreciate it if you cite the following paper when using the dataset:\r\nQiang Hao, Rui Cai, Zhiwei Li, Lei Zhang, Yanwei Pang, Feng Wu, and Yong Rui. \"Efficient 2D-to-3D Correspondence Filtering for Scalable 3D Object Recognition<\/a>\". in Proc. of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), pp.899-906, Portland, Oregon, USA. June 23-28, 2013.\r\n

Contact<\/h3>\r\nIf you have questions about the dataset, please contact Qiang Hao (email: haoq@live.com<\/a>)."}],"slides":[],"related-researchers":[],"msr_research_lab":[199560],"msr_impact_theme":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/285590"}],"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":0,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/285590\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=285590"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=285590"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=285590"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=285590"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=285590"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}