000201014 001__ 201014
000201014 005__ 20190812205805.0
000201014 037__ $$aCONF
000201014 245__ $$aWorldwide Pose Estimation Using 3D Point Clouds
000201014 269__ $$a2012
000201014 260__ $$c2012
000201014 336__ $$aConference Papers
000201014 520__ $$aWe address the problem of determining where a photo was taken by estimating a full 6-DOF-plus-intrincs camera pose with respect to a large geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose estimation. Our method scales to datasets with hundreds of thousands of images and tens of millions of 3D points through the use of two new techniques: a co-occurrence prior for RANSAC and bidirectional matching of image features with 3D points. We evaluate our method on several large data sets, and show state-of-the-art results on landmark recognition as well as the ability to locate cameras to within meters, requiring only seconds per query.
000201014 700__ $$aLI, Yunpeng
000201014 700__ $$aSnavely, Noah
000201014 700__ $$aHuttenlocher, Dan
000201014 700__ $$g112366$$aFua, Pascal$$0240252
000201014 7112_ $$dOctober, 2012$$cFlorence, Italy$$aEuropean Conference on Computer Vision (ECCV)
000201014 8564_ $$zn/a$$yn/a$$uhttps://infoscience.epfl.ch/record/201014/files/global_pose.pdf$$s292630
000201014 909C0 $$xU10659$$pCVLAB$$0252087
000201014 909CO $$ooai:infoscience.tind.io:201014$$qGLOBAL_SET$$pconf$$pIC
000201014 917Z8 $$x112366
000201014 937__ $$aEPFL-CONF-201014
000201014 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000201014 980__ $$aCONF