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LRM: Large Reconstruction Model for Single Image to 3D
International Conference on Learning Representations, 2023We propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category ...
Yicong Hong +9 more
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Reconstruction Algorithms for Electromagnetic Imaging [PDF]
IEEE Transactions on Instrumentation and Measurement, 2003 Two algorithms for image reconstruction in electromagnetic imaging are proposed. The first approach concerns the application of a hybrid version of the genetic algorithm to tomographic imaging of dielectric configurations. In the second approach, buried inhomogeneities are schematized as multilayer infinite dielectric cylinders with elliptic cross ...
PASTORINO, MATTEO +3 more
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Physics in Medicine and Biology, 2006
We give an overview of the role of Physics in Medicine and Biology in the development of tomographic reconstruction algorithms. We focus on imaging modalities involving ionizing radiation, CT, PET and SPECT, and cover a wide spectrum of reconstruction problems, starting with classical 2D tomography in the 1970s up to 4D and 5D problems involving ...
Defrise, Michel, Gullberg, Grant
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We give an overview of the role of Physics in Medicine and Biology in the development of tomographic reconstruction algorithms. We focus on imaging modalities involving ionizing radiation, CT, PET and SPECT, and cover a wide spectrum of reconstruction problems, starting with classical 2D tomography in the 1970s up to 4D and 5D problems involving ...
Defrise, Michel, Gullberg, Grant
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Accelerated image reconstruction using ordered subsets of projection data
IEEE Trans. Medical Imaging, 1994The authors define ordered subset processing for standard algorithms (such as expectation maximization, EM) for image restoration from projections. Ordered subsets methods group projection data into an ordered sequence of subsets (or blocks).
Malcolm Hudson +1 more
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Image reconstruction by domain-transform manifold learning
Nature, 2017Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio ...
Bo Zhu +3 more
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Reconstruction in diffraction imaging
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 1989The problem of reconstruction in imaging systems that are modeled using the Helmholtz wave equation (diffraction imaging) is addressed. A spectral analysis of the available diffraction data is presented to help develop algorithms and constraints on a diffraction imaging system's parameters for accurate reconstruction of the desired image.
M. Soumekh, J.-H. Choi
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Deep learning in magnetic resonance image reconstruction
Journal of Medical Imaging and Radiation Oncology, 2021Magnetic resonance (MR) imaging visualises soft tissue contrast in exquisite detail without harmful ionising radiation. In this work, we provide a state‐of‐the‐art review on the use of deep learning in MR image reconstruction from different image ...
S. Chandra +5 more
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Fast Image Reconstruction with an Event Camera
IEEE Workshop/Winter Conference on Applications of Computer Vision, 2020Event cameras are powerful new sensors able to capture high dynamic range with microsecond temporal resolution and no motion blur. Their strength is detecting brightness changes (called events) rather than capturing direct brightness images; however ...
C. Scheerlinck +5 more
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