Results 51 to 60 of about 412,052 (160)

A Survey on Deep Learning in 3D CAD Reconstruction

Applied Sciences
Three-dimensional CAD reconstruction is a long-standing and important task in fields such as industrial manufacturing, architecture, medicine, film and television, research, and education.
Ruiquan Lin, Yunwei Ji, Wanting Ding, Tianxiang Wu, Yaosheng Zhu, Mengxi Jiang   +5 more
doaj   +1 more source

Weakly supervised 3D Reconstruction with Adversarial Constraint

, 2017
Supervised 3D reconstruction has witnessed a significant progress through the use of deep neural networks. However, this increase in performance requires large scale annotations of 2D/3D data.
Chandraker, Manmohan, Choy, Christopher B., Garg, Animesh, Gwak, JunYoung, Savarese, Silvio   +4 more
core   +1 more source

Deep learning versus $\ell^1$-minimization for compressed sensing photoacoustic tomography

, 2019
We investigate compressed sensing (CS) techniques for reducing the number of measurements in photoacoustic tomography (PAT). High resolution imaging from CS data requires particular image reconstruction algorithms.
Antholzer, Stephan, Haltmeier, Markus, Schwab, Johannes   +2 more
core   +1 more source

Hierarchical distillation for image compressive sensing reconstruction

Electronics Letters, 2021
Compressive sensing (CS) is an effective algorithm for reconstructing images from a small sample of data. CS models combining traditional optimisation‐based CS methods and deep learning have been used to improve image reconstruction performance. However,
Bokyeung Lee, Bonhwa Ku, Wanjin Kim, Hanseok Ko   +3 more
doaj   +1 more source

Learned Primal-dual Reconstruction

, 2018
We propose the Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal ...
Adler, Jonas, Öktem, Ozan
core   +1 more source

Synthetic Data Generation Pipeline for Multi-Task Deep Learning-Based Catheter 3D Reconstruction and Segmentation from Biplanar X-Ray Images

Applied Sciences
Catheter three-dimensional (3D) position reconstruction is a technology that reconstructs spatial positions from multiple two-dimensional (2D) images.
Junang Wang, Guixiang Zhang, Wenyun Yang, Changsheng Wang, Jinbo Yang   +4 more
doaj   +1 more source

MR image reconstruction using deep density priors

, 2018
Algorithms for Magnetic Resonance (MR) image reconstruction from undersampled measurements exploit prior information to compensate for missing k-space data.
Baumgartner, Christian F., Konukoglu, Ender, Luechinger, Roger, Pruessmann, Klaas P., Tezcan, Kerem C.   +4 more
core   +1 more source

A Deep Learning Reconstruction Framework for Differential Phase-Contrast Computed Tomography with Incomplete Data

, 2019
Differential phase-contrast computed tomography (DPC-CT) is a powerful analysis tool for soft-tissue and low-atomic-number samples. Limited by the implementation conditions, DPC-CT with incomplete projections happens quite often.
Dong, Jianbing, Fu, Jian, He, Zhao
core   +1 more source

Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning

, 2017
Antihydrogen is at the forefront of antimatter research at the CERN Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry and antigravity effects require the efficient detection of antihydrogen annihilation events, which is ...
Ananya, Baldi, Pierre, Radics, Balint, Sadowski, Peter, Yamazaki, Yasunori   +4 more
core   +1 more source

Three-Dimensional Dense Reconstruction: A Review of Algorithms and Datasets

Sensors
Three-dimensional dense reconstruction involves extracting the full shape and texture details of three-dimensional objects from two-dimensional images. Although 3D reconstruction is a crucial and well-researched area, it remains an unsolved challenge in ...
Yangming Lee
doaj   +1 more source