A System for Near Real-Time 3D Reconstruction from Multi-view Using 4G Enabled Mobile

2014 ◽  
Author(s):  
Arindam Saha ◽  
Brojeshwar Bhowmick ◽  
Aniruddha Sinha
Keyword(s):  
3D Reconstruction ◽  
Real Time

scholarly journals Real-time 3D reconstruction of non-rigid shapes with a single moving camera

2016 ◽  
Vol 153 ◽  
pp. 37-54 ◽  
Author(s):  
Antonio Agudo ◽  
Francesc Moreno-Noguer ◽  
Begoña Calvo ◽  
J.M.M. Montiel
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Moving Camera

scholarly journals A Smartphone Interface for a Wireless EEG Headset with Real-Time 3D Reconstruction

2011 ◽  
pp. 317-318 ◽  
Author(s):  
Arkadiusz Stopczynski ◽  
Jakob Eg Larsen ◽  
Carsten Stahlhut ◽  
Michael Kai Petersen ◽  
Lars Kai Hansen
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Wireless Eeg

A Robust and Real-Time Full 3D Reconstruction Method Based on Multiple Kinect

2018 ◽  
pp. 1420-1428
Author(s):  
Xiongfeng Peng ◽  
Liaoyuan Zeng ◽  
Wenyi Wang ◽  
Zhili Liu ◽  
Yifeng Yang ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Reconstruction Method

scholarly journals Real-Time 3D Reconstruction of Thin Surface Based on Laser Line Scanner

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 534 ◽  
Author(s):  
Yuan He ◽  
Shunyi Zheng ◽  
Fengbo Zhu ◽  
Xia Huang
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Laser Line ◽  
Frame Rate ◽  
Processing Unit ◽  
Memory Usage ◽  
Central Processing ◽  
Time Performance ◽  
Topological Errors ◽  
Line Scanner

The truncated signed distance field (TSDF) has been applied as a fast, accurate, and flexible geometric fusion method in 3D reconstruction of industrial products based on a hand-held laser line scanner. However, this method has some problems for the surface reconstruction of thin products. The surface mesh will collapse to the interior of the model, resulting in some topological errors, such as overlap, intersections, or gaps. Meanwhile, the existing TSDF method ensures real-time performance through significant graphics processing unit (GPU) memory usage, which limits the scale of reconstruction scene. In this work, we propose three improvements to the existing TSDF methods, including: (i) a thin surface attribution judgment method in real-time processing that solves the problem of interference between the opposite sides of the thin surface; we distinguish measurements originating from different parts of a thin surface by the angle between the surface normal and the observation line of sight; (ii) a post-processing method to automatically detect and repair the topological errors in some areas where misjudgment of thin-surface attribution may occur; (iii) a framework that integrates the central processing unit (CPU) and GPU resources to implement our 3D reconstruction approach, which ensures real-time performance and reduces GPU memory usage. The proposed results show that this method can provide more accurate 3D reconstruction of a thin surface, which is similar to the state-of-the-art laser line scanners with 0.02 mm accuracy. In terms of performance, the algorithm can guarantee a frame rate of more than 60 frames per second (FPS) with the GPU memory footprint under 500 MB. In total, the proposed method can achieve a real-time and high-precision 3D reconstruction of a thin surface.

scholarly journals Real-Time Object Tracking and 3D Reconstruction by Active Camera

1999 ◽  
Vol 35 (5) ◽  
pp. 675-683 ◽  
Author(s):  
Koichiro DEGUCHI ◽  
Shingo KAGAMI ◽  
Satoshi SAGA ◽  
Hidekata HONTANI
Keyword(s):  
3D Reconstruction ◽  
Object Tracking ◽  
Real Time ◽  
Active Camera

scholarly journals Near Real-Time 3D Reconstruction from InIm Video Stream

2008 ◽  
pp. 336-347 ◽  
Author(s):  
D. Chaikalis ◽  
G. Passalis ◽  
N. Sgouros ◽  
D. Maroulis ◽  
T. Theoharis
Keyword(s):  
3D Reconstruction ◽  
Real Time ◽  
Video Stream
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