000164425 001__ 164425
000164425 005__ 20181203022330.0
000164425 0247_ $$2doi$$a10.1007/s00138-011-0346-8
000164425 022__ $$a0932-8092
000164425 02470 $$2ISI$$a000307539300006
000164425 037__ $$aARTICLE
000164425 245__ $$aEfficient Large Scale Multi-View Stereo for Ultra High Resolution Image Sets
000164425 269__ $$a2012
000164425 260__ $$aNew York$$bSpringer Verlag$$c2012
000164425 300__ $$a18
000164425 336__ $$aJournal Articles
000164425 520__ $$aWe present a new approach for large scale multi-view stereo matching, which is   designed to operate on ultra high resolution image sets and efficiently   compute dense 3D point clouds. We show that, by using a robust descriptor for   matching purposes and high resolution images, we can skip the computationally   expensive steps other algorithms require. As a result, our method has low   memory requirements and low computational complexity while producing 3D point   clouds containing virtually no outliers. This makes it exceedingly suitable   for large scale reconstruction.  The core of our algorithm is the dense   matching of image pairs using DAISY descriptors, implemented so as to   eliminate redundancies and optimize memory access. We use a variety of   challenging data sets to validate and compare our results against other   algorithms.
000164425 6531_ $$amulti-view stereo, 3D reconstruction, high-resolution
000164425 700__ $$0242709$$aTola, Engin$$g170333
000164425 700__ $$0244088$$aStrecha, Christoph$$g182325
000164425 700__ $$0240252$$aFua, Pascal$$g112366
000164425 773__ $$j23$$k5$$tMachine Vision and Applications
000164425 8564_ $$uhttp://www.engintola.com/research/emvs.html$$zURL
000164425 8564_ $$s3226037$$uhttps://infoscience.epfl.ch/record/164425/files/mvap_1.pdf$$yn/a$$zn/a
000164425 909C0 $$0252087$$pCVLAB$$xU10659
000164425 909CO $$ooai:infoscience.tind.io:164425$$pIC$$particle
000164425 917Z8 $$x112366
000164425 917Z8 $$x170333
000164425 917Z8 $$x170333
000164425 917Z8 $$x112366
000164425 917Z8 $$x112366
000164425 937__ $$aEPFL-ARTICLE-164425
000164425 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000164425 980__ $$aARTICLE