folder 1dsfm (18 files)
fileSfM_Init-master.zip 38.05kB
fileimages.Yorkminster.tar 2.30GB
fileimages.Union_Square.tar 3.80GB
fileimages.Vienna_Cathedral.tar 3.45GB
file1DSfM_ECCV14.pdf 11.61MB
fileimages.Roman_Forum.tar 1.59GB
fileimages.Tower_of_London.tar 1.08GB
fileimages.Piccadilly.tar 3.97GB
fileimages.NYC_Library.tar 1.69GB
fileimages.Piazza_del_Popolo.tar 1.52GB
fileimages.Montreal_Notre_Dame.tar 1.63GB
fileimages.Madrid_Metropolis.tar 740.75MB
fileimages.Ellis_Island.tar 1.62GB
fileimages.Gendarmenmarkt.tar 1.03GB
filedatasets.tar.gz 682.12MB
fileimages.Alamo.tar 2.09GB
fileimages.Trafalgar.tar 9.11GB
file1DSfM_poster.pdf 25.82MB
Type: Dataset
Tags: Dataset, photos, structure from motion, landmark, cornell, photo, paper, code, photographs, sfm

Bibtex:
@inproceedings{wilson_eccv2014_1dsfm,
title= {1dsfm},
author= {Kyle Wilson and Noah Snavely},
booktitle= {Proceedings of the European Conference on Computer Vision ({ECCV})},
year= {2014},
abstract= {We present a simple, effective method for solving structure from motion problems by averaging epipolar geometries. Based on recent successes in solving for global camera rotations using averaging schemes, we focus on the problem of solving for 3D camera translations given a network of noisy pairwise camera translation directions (or 3D point observations). To do this well, we have two main insights. First, we propose a method for removing outliers from problem instances by solving simpler low-dimensional subproblems, which we refer to as 1DSfM problems. Second, we present a simple, principled averaging scheme. We demonstrate this new method in the wild on Internet photo collections. 

Dataset scraped 23 February 2019

Code and papers scraped 15 July 2022},
keywords= {Dataset, photos, structure from motion, landmark, cornell, photo, paper, code, photographs, sfm},
terms= {},
license= {},
superseded= {},
url= {https://research.cs.cornell.edu/1dsfm/}
}


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