Daniel Scharstein

Publications55
h-index29
Citations16,314
Highly Influential Citations1,497
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A Database and Evaluation Methodology for Optical Flow
TLDR
This paper proposes a new set of benchmarks and evaluation methods for the next generation of optical flow algorithms and analyzes the results obtained to date to draw a large number of conclusions.
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A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
TLDR
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.
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A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
TLDR
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.
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High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth
We present a structured lighting system for creating high-resolution stereo datasets of static indoor scenes with highly accurate ground-truth disparities. The system includes novel techniques for…
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High-accuracy stereo depth maps using structured light
TLDR
A method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using structured light that does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors.
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A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors
TLDR
A set of energy minimization benchmarks are described and used to compare the solution quality and runtime of several common energy minimizations algorithms and a general-purpose software interface is provided that allows vision researchers to easily switch between optimization methods.
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Evaluation of Cost Functions for Stereo Matching
TLDR
This paper evaluates the insensitivity of different matching costs with respect to radiometric variations of the input images with a local, a semi-global, and a global stereo method.
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Evaluation of Stereo Matching Costs on Images with Radiometric Differences
TLDR
Among the best costs are BilSub, which performs consistently very well for low radiometric differences; HMI, which is slightly better as pixelwise matching cost in some cases and for strong image noise; and Census, which showed the best and most robust overall performance.
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Learning Conditional Random Fields for Stereo
TLDR
This paper has constructed a large number of stereo datasets with ground-truth disparities, and a subset of these datasets are used to learn the parameters of conditional random fields (CRFs) and presents experimental results illustrating the potential of this approach for automatically learning the Parameters of models with richer structure than standard hand-tuned MRF models.
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A Comparative Study of Energy Minimization Methods for Markov Random Fields
TLDR
A set of energy minimization benchmarks, which are used to compare the solution quality and running time of several common energy minimizations algorithms, as well as a general-purpose software interface that allows vision researchers to easily switch between optimization methods with minimal overhead.
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