A fast 3D reconstruction algorithm for inverse-geometry CT based on an exact PET rebinning algorithm

2007 ◽  
Author(s):  
Samuel R. Mazin ◽  
Norbert J. Pelc
Keyword(s):  
3D Reconstruction ◽  
Reconstruction Algorithm ◽  
Inverse Geometry

scholarly journals An Adaptation of the Wiener Filter Suitable for Analyzing Images of Isolated Single Particles, and Accompanying 3D Reconstruction Algorithm

2011 ◽  
Vol 17 (S2) ◽  
pp. 86-87
Author(s):  
C Sindelar ◽  
N Grigorieff
Keyword(s):  
3D Reconstruction ◽  
Wiener Filter ◽  
Reconstruction Algorithm ◽  
Single Particles

Extended abstract of a paper presented at Microscopy and Microanalysis 2011 in Nashville, Tennessee, USA, August 7–August 11, 2011.

Трехмерная рентгеновская томография промышленных объектов при ограниченных размерах регистрирующего экрана

2021 ◽  
pp. 54-62
Author(s):  
В.П. Карих ◽  
Б.В. Певченко ◽  
А.В. Курбатов ◽  
А.А. Охотников ◽  
А.А. Скоков
Keyword(s):  
3D Reconstruction ◽  
Cross Section ◽  
Test Object ◽  
Reconstruction Algorithm ◽  
Scanning Method ◽  
Spiral Scanning ◽  
Arbitrarily Oriented Crack ◽  
Principal Possibility

The article investigates the possibilities of using a 3D tomograph with a limited-sizes registering screen for detecting arbitrarily oriented crack-like defects in large industrial objects. Circular and spiral scanning schemes are considered, the principal possibility of detecting defects in the case of two-pass spiral scanning and a registering screen covering half of the view field of the test object cross-section is shown. The performance of the 3D reconstruction algorithm for the selected scanning method has been demonstrated.

Structural light 3D reconstruction algorithm based on deep learning

2019 ◽  
Author(s):  
Haibin Niu ◽  
Limin Hu ◽  
Shi Yan ◽  
Lei Ning ◽  
Yang Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
Reconstruction Algorithm

CROWD-SOURCED VISUAL DATA COLLECTION FOR MONITORING INDOOR CONSTRUCTION IN 3D

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Fouad Amer ◽  
Mani Golparvar-Fard
Keyword(s):  
Data Collection ◽  
3D Reconstruction ◽  
Structure From Motion ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
Reconstruction Method ◽  
Visual Data ◽  
Mapping Algorithm ◽  
3D Point Clouds ◽  
Localization And Mapping

Complete and accurate 3D monitoring of indoor construction progress using visual data is challenging. It requires (a) capturing a large number of overlapping images, which is time-consuming and labor-intensive to collect, and (b) processing using Structure from Motion (SfM) algorithms, which can be computationally expensive. To address these inefficiencies, this paper proposes a hybrid SfM-SLAM 3D reconstruction algorithm along with a decentralized data collection workflow to map indoor construction work locations in 3D and any desired frequency. The hybrid 3D reconstruction method is composed of a pipeline of Structure from Motion (SfM) coupled with Multi-View Stereo (MVS) to generate 3D point clouds and a SLAM (Simultaneous Localization and Mapping) algorithm to register the separately formed models together. Our SfM and SLAM pipelines are built on binary Oriented FAST and Rotated BRIEF (ORB) descriptors to tightly couple these two separate reconstruction workflows and enable fast computation. To elaborate the data capture workflow and validate the proposed method, a case study was conducted on a real-world construction site. Compared to state-of-the-art methods, our preliminary results show a decrease in both registration error and processing time, demonstrating the potential of using daily images captured by different trades coupled with weekly walkthrough videos captured by a field engineer for complete 3D visual monitoring of indoor construction operations.

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