Computer Vision – ECCV 2012: 12th European Conference on by Oren Freifeld, Michael J. Black (auth.), Andrew Fitzgibbon,

By Oren Freifeld, Michael J. Black (auth.), Andrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid (eds.)

The seven-volume set comprising LNCS volumes 7572-7578 constitutes the refereed complaints of the twelfth eu convention on computing device imaginative and prescient, ECCV 2012, held in Florence, Italy, in October 2012. The 408 revised papers provided have been conscientiously reviewed and chosen from 1437 submissions. The papers are geared up in topical sections on geometry, 2nd and 3D form, 3D reconstruction, visible reputation and type, visible gains and snapshot matching, visible tracking: motion and actions, types, optimisation, studying, visible monitoring and photograph registration, photometry: lights and color, and photo segmentation.

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Extra info for Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part I

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SF-1 refers to the San Francisco data set with image histogram equalization and upright SIFT features [7], while SF-0 is the one without. Note that SF-0 and SF-1 are derived from the same image set. Images in 3D model Landmarks 205,162 SF-0 610,773 SF-1 790,409 Points in Test Images in 3D model images 3D model 38,190,865 10,000 Quad 4,830 30,342,328 803 Dubrovnik [8] 6,044 75,410,077 803 Rome [8] 15,179 Points in Test 3D model images 2,022,026 348 1,975,263 800 4,067,119 1,000 image retrieval or, in our case, pose estimation.

The block diagonal structure in Eq. (5) enables easy computation: If Nt ∈ M , i ∈ {1, 2} and gi,j (Ri , Gi , Si )j ∈ GT , then pi = {gi,j }j=1 Nt d(p1 , p2 )2 = dGT (g1,j , g2,j )2 (6) j=1 where, for (g1 , g2 ) ∈ GT × GT (dropping the j notation), dGT (g1 , g2 )2 log(R1T R2 ) 2 F + log(A−1 1 A2 ) 2 F + |log(S2 /S1 )|2 . , if Q1 and Q2 are two deformation matrices, one would 10 O. J. Black like to have Q1 − I F = Q2 Q1 − Q2 I F but this rarely holds (consider the case where Q1 ∈ SO(3) and Q2 = 2I).

More similar to our approach are methods that leverage results of SfM techniques. Irschara et al. [22] use SfM reconstructions to generate a set of “virtual” images that cover a scene, then index these as documents using BoW methods. Direct 2D-to-3D approaches have recently been used to establish correspondence between a query image and a reconstructed 3D model, bypassing an intermediate image retrieval step [8, 9]. While “inverse matching” from 3D points to image features [8] can sometimes find correct matches very quickly though search prioritization, results with this method becomes more difficult on the very large models we consider here.

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