scholarly journals High-Precision 3D Reconstruction of Cooperative Markers under Motion Blur

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yun Shi ◽  
Cong Tao ◽  
Xiaoping Wang ◽  
Liyan Zhang
Keyword(s):  
3D Reconstruction ◽  
High Precision ◽  
Exposure Period ◽  
Motion Blur ◽  
Generation Model ◽  
Image Generation ◽  
Reconstruction Accuracy ◽  
Simulation Based ◽  
Blurred Image ◽  
Moving Path

The application of artificial intelligence and deep learning in the fields of wireless communication, image and speech recognition, and 3D reconstruction has successfully solved some difficult modeling problems. This paper focuses on the high-precision 3D reconstruction of the motion-blurred cooperative markers, including the Chinese character coded targets (CCTs) and the noncoded circular markers. A simulation-based motion-blurred image generation model is constructed to provide sufficient samples for training the convolutional neural network to identify and match the motion-blurred CCTs on the moving object. The blurred noncoded marker matching is performed through homography. The 3D reconstruction of the markers is realized via the optimization of the spatial moving path within the exposure period. The midpoint of the moving path of the markers is taken as the final reconstruction result. The experimental results show that the 3D reconstruction accuracy of the markers with a certain motion blur effect is about 0.08 mm.

The Restoration of Motion Blurred Images Based on the Background Modeling

2014 ◽  
Vol 687-691 ◽  
pp. 3591-3595
Author(s):  
Jiang Yang Chen ◽  
Xi Ling Luo
Keyword(s):  
Wiener Filter ◽  
Background Modeling ◽  
Motion Blur ◽  
Edge Density ◽  
Image Generation ◽  
Point Spread ◽  
Spread Function ◽  
Blurred Image ◽  
Image Area ◽  
Blurred Images

For the mutual effects of camera shake and subject movement, the image generation space varying motion blur. In order to achieve image restoration, firstly dividing the image area using the Gaussian background modeling, and updated model adaptive to improve the speed and convergence accuracy. Then use the total variation (TV) of the L1 model to estimate the regional point spread function (PSF), and adopted the edge density weight to reduce small edge’s interference for the PSF estimates. Eventually to restored image by Wiener filter. Through experimental analysis, compared with other algorithms, our algorithms get better results in the space varies motion-blurred image.

scholarly journals Towards a Fully Automated 3D Reconstruction System Based on LiDAR and GNSS in Challenging Scenarios

2021 ◽  
Vol 13 (10) ◽  
pp. 1981
Author(s):  
Ruike Ren ◽  
Hao Fu ◽  
Hanzhang Xue ◽  
Zhenping Sun ◽  
Kai Ding ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Precision ◽  
Moving Objects ◽  
Autonomous Driving ◽  
Satellite System ◽  
Factor Graph ◽  
Mapping System ◽  
Global Navigation ◽  
Real World Datasets ◽  
Global Navigation Satellite

High-precision 3D maps play an important role in autonomous driving. The current mapping system performs well in most circumstances. However, it still encounters difficulties in the case of the Global Navigation Satellite System (GNSS) signal blockage, when surrounded by too many moving objects, or when mapping a featureless environment. In these challenging scenarios, either the global navigation approach or the local navigation approach will degenerate. With the aim of developing a degeneracy-aware robust mapping system, this paper analyzes the possible degeneration states for different navigation sources and proposes a new degeneration indicator for the point cloud registration algorithm. The proposed degeneracy indicator could then be seamlessly integrated into the factor graph-based mapping framework. Extensive experiments on real-world datasets demonstrate that the proposed 3D reconstruction system based on GNSS and Light Detection and Ranging (LiDAR) sensors can map challenging scenarios with high precision.

Underwater high-precision panoramic 3D image generation

2020 ◽  
Author(s):  
Zhaolun Li ◽  
Rushi Lan ◽  
Zhuo Chen ◽  
Xiaonan Luo ◽  
Ji Li ◽  
...  
Keyword(s):  
High Precision ◽  
Image Generation ◽  
3D Image

scholarly journals A 3D Reconstruction Pipeline of Urban Drainage Pipes Based on MultiviewImage Matching Using Low-Cost Panoramic Video Cameras

Water ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 2101 ◽  
Author(s):  
Zhang ◽  
Zhao ◽  
Hu ◽  
Wang ◽  
Ai ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
High Precision ◽  
Image Matching ◽  
Low Cost ◽  
Geometric Distortion ◽  
Urban Drainage ◽  
Affine Invariant ◽  
Pipe Inspection ◽  
Video Cameras ◽  
Panoramic Video

Urban drainage pipe networks have complex spatial contributions, andthey are now facing problems such as damage, defects, and aging. A rapid and high-precision pipe inspection strategy is thekey to ensuring thesustainable development of urban water supply and drainage system. In this paper, a three-dimensional (3D) reconstruction pipeline of urban drainage pipes based on multiview image matching using low-cost panoramic video cameras is proposed, which provides an innovative technical approach for pipe inspection. Firstly, we extracted frames from the panoramic video of the pipes andcorrected the geometric distortion using a spherical reprojection to obtain multiview pipe images. Second, the robust feature matching method using support lines and affine-invariant ratios isintroduced to conduct pipe image matching. Finally, the photogrammetric processing, using structure from motion (SfM) and dense reconstruction, wasintroduced to achieve the 3D modeling of drainage pipes. Several typical drainage pipes and shafts of the real scenes were taken for the 3D reconstruction experiments. Theresults show that our strategy can realize high-precision 3D reconstruction of different types of pipes, which can provide effective technical support for rapid and efficient inspection of urban pipes with broad application prospects in the daily management of sustainable urban drainage systems (SUDSs).

scholarly journals Fast Motion Deblurring Using Sensor-Aided Motion Trajectory Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Eunsung Lee ◽  
Eunjung Chae ◽  
Hejin Cheong ◽  
Joonki Paik
Keyword(s):  
Inertial Sensors ◽  
Motion Blur ◽  
Trajectory Estimation ◽  
Motion Trajectories ◽  
Motion Deblurring ◽  
Spread Function ◽  
Blurred Image ◽  
Fast Motion ◽  
Noise Amplification ◽  
Spatially Varying

This paper presents an image deblurring algorithm to remove motion blur using analysis of motion trajectories and local statistics based on inertial sensors. The proposed method estimates a point-spread-function (PSF) of motion blur by accumulating reweighted projections of the trajectory. A motion blurred image is then adaptively restored using the estimated PSF and spatially varying activity map to reduce both restoration artifacts and noise amplification. Experimental results demonstrate that the proposed method outperforms existing PSF estimation-based motion deconvolution methods in the sense of both objective and subjective performance measures. The proposed algorithm can be employed in various imaging devices because of its efficient implementation without an iterative computational structure.

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