Unsupervised 3D object recognition and reconstruction in unordered datasets

By Matthew A. Brown, David G. Lowe. Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM'05), 2005

317 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e30dcbc6998e1710c342fedffd440babaabdfd01

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In Aerospace Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Multi-View Stereo for Community Photo Collections (2007, methodology): “This surface growing approach takes as input sparse feature points, leveraging the success of structure-from-motion techniques [2, 23] which produce such output and have recently been demonstrated to operate effectively on CPCs.”
CZECH TECHNICAL UNIVERSITY IN PRAGUE (2005, background): “Most of the state of the art techniques for 3D reconstruction from unordered image sets [35, 3, 42, 24] start the computation by performing exhaustive pairwise image matching in order to reveal the structure and connectivity of the data.”
Evaluation of image-based modeling and laser scanning accuracy for emerging automated performance monitoring techniques (2011, methodology): “Recent methods have taken advantage of these properties [15], [16], [31], and [32].”
Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application (2012, methodology): “SfM algorithms were originally developed for the automatic generation of 3D models from unordered image collections [ Brown and Lowe , 2005], for example, color reconstructions of iconic buildings using only images downloaded from the Internet [e.g., Snavely et al. , 2006, 2008a].”
Skeletal graphs for efficient structure from motion (2008, background): “Recent results have demonstrated [20, 4, 21, 25] that it is possible to adapt structure from motion (SfM) methods, originally developed for video, to operate successfully on such unstructured collections.”
A multi-view probabilistic model for 3D object classes (2009, methodology): “Notably, [3, 32, 8, 24] have proposed methods for identifying and recognizing single objects such as a cereal box or a Teddy bear, under arbitrary viewing conditions.”
3D generic object categorization, localization and pose estimation (2007, background): “[3] pioneered unsupervised recognition and reconstruction of object instances from an unlabeled set of images.”
Modeling the World from Internet Photo Collections (2008, methodology): “The SfM approach used in this paper is similar to that of Brown and Lowe (2005), with several modifications to improve robustness over a variety of data sets.”