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Richard Szeliski
Researcher at Facebook
Publications - 377
Citations - 76466
Richard Szeliski is an academic researcher from Facebook. The author has contributed to research in topics: Pixel & Motion estimation. The author has an hindex of 113, co-authored 359 publications receiving 72019 citations. Previous affiliations of Richard Szeliski include Carnegie Mellon University & University of Washington.
Papers
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Journal ArticleDOI
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
TL;DR: This paper has designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms.
Book
Computer Vision: Algorithms and Applications
TL;DR: Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images and takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene.
Journal ArticleDOI
Photo tourism: exploring photo collections in 3D
TL;DR: This work presents a system for interactively browsing and exploring large unstructured collections of photographs of a scene using a novel 3D interface that consists of an image-based modeling front end that automatically computes the viewpoint of each photograph and a sparse 3D model of the scene and image to model correspondences.
Proceedings ArticleDOI
The lumigraph
TL;DR: A new method for capturing the complete appearance of both synthetic and real world objects and scenes, representing this information, and then using this representation to render images of the object from new camera positions.
Proceedings ArticleDOI
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
TL;DR: This paper first survey multi-view stereo algorithms and compare them qualitatively using a taxonomy that differentiates their key properties, then describes the process for acquiring and calibrating multiview image datasets with high-accuracy ground truth and introduces the evaluation methodology.