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Features matching • SIFT descriptor: 128-dimension vector of integers • Matching: try to identify corresponding features for each pair of images - relationship among photos X = 27/11/2008 Fundamentals and Trends in Image Processing – <strong>IMPA</strong> cpalomo@inf.puc-rio.br 10
Robust features matching • Naive approach for matching: brute-force computation of all distances for complete list of features for each pair of images • Better try: use Mount’s approximate nearest neighbors library to speed up search – Data structures and algorithms for exact and approximate nearest neighbor searching in arbitrarily high dimensions – Based on kd-trees and box-decomposition trees – Distances measured using any class of distance functions called Minkowski metrics 27/11/2008 Fundamentals and Trends in Image Processing – <strong>IMPA</strong> cpalomo@inf.puc-rio.br 11
Page 1 and 2: Exploring Community Photo Collectio
Page 3 and 4: Characteristics of CPC’s Opportun
Page 5 and 6: Proposal Input photos Scene Reconst
Page 7 and 8: Method - Step 1 1. Features/keypoin
Page 9: Method - Step 2 1. Features/keypoin
Page 13 and 14: Fundamental matrix • Computer vis
Page 15 and 16: F-matrix estimation: normalization
Page 17 and 18: RANSAC - RANdom SAmple Consensus
Page 19 and 20: RANSAC for F-matrix estimation Repe
Page 21 and 22: Outliers removal by RANSAC • RANS
Page 23 and 24: Structure from motion (SfM) p 1 p 4
Page 25 and 26: Bundle adjustment code • Noah Sna
Page 27 and 28: Results • Point cloud and relativ
Page 29 and 30: Example of bad convergence to a loc
Page 31 and 32: References • Photo tourism: Explo

pdf file, 3 MB - Luiz Velho - IMPA

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