2009 · 1.1K citations · 16 references
ComputingComputer ScienceComputational GeometryComputer VisionMachine Vision3D Imaging3D ReconstructionGeometry ProcessingMulti-view GeometryPhotogrammetryScene ReconstructionCity ReconstructionImage MatchingLarge-scale Computer VisionMulti-view ReconstructionScene Reconstruction From Images
The paper introduces a system that matches and reconstructs 3D scenes from massive photo collections of cities such as Rome. The system employs novel parallel distributed matching and reconstruction algorithms that maximize parallelism, minimize bottlenecks, and scale with problem size and compute resources. Experiments show that 150,000 images of a city can be reconstructed in under a day using 500 compute cores.
We present a system that can match and reconstruct 3D scenes from extremely large collections of photographs such as those found by searching for a given city (e.g., Rome) on Internet photo sharing sites. Our system uses a collection of novel parallel distributed matching and reconstruction algorithms, designed to maximize parallelism at each stage in the pipeline and minimize serialization bottlenecks. It is designed to scale gracefully with both the size of the problem and the amount of available computation. We have experimented with a variety of alternative algorithms at each stage of the pipeline and report on which ones work best in a parallel computing environment. Our experimental results demonstrate that it is now possible to reconstruct cities consisting of 150 K images in less than a day on a cluster with 500 compute cores.
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Video Google: a text retrieval approach to object matching in videos
Scalable Recognition with a Vocabulary Tree
D. Nistér, Henrik Stewénius · 2006 · 3.6K citations
Noah Snavely, Steven M. Seitz, Richard Szeliski · ACM Transactions on Graphics · 2006 · 2.8K citations · Full text