scholarly journals Real-Time Detection of Planar Regions in Unorganized Point Clouds

2020 ◽  
Author(s):  
Frederico Limberger ◽  
Manuel Oliveira
Keyword(s):  
Real Time ◽  
Point Clouds ◽  
Gaussian Kernel ◽  
Region Detection ◽  
Laser Scanners ◽  
Computer Vision Applications ◽  
Voting Scheme ◽  
Real Time Applications ◽  
Planar Regions ◽  
Better Than

Automatic detection of planar regions in point clouds is an important step for many graphics, image processing, and computer vision applications. While laser scanners and digital photography have allowed us to capture increasingly larger datasets, previous approaches for planar region detection are computationally expensive, precluding their use in real-time applications. We present an O(n log n) technique for plane detection in unorganized point clouds based on an efficient Hough-transform voting scheme. It works by clustering sets of approximately co-planar points and by casting votes for these clusters on a spherical accumulator using a trivariate Gaussian kernel. A comparison with competing techniques shows that our approach is considerably faster and scales significantly better than previous ones, being the first practical solution for deterministic plane detection in large unorganized point clouds.

Image Completion Using Randomized Correspondence

2011 ◽  
Vol 271-273 ◽  
pp. 229-234
Author(s):  
Yun Ling ◽  
Hai Tao Sun ◽  
Jian Wei Han ◽  
Xun Wang
Keyword(s):  
Real Time ◽  
Random Sampling ◽  
Nearest Neighbor ◽  
Structural Constraint ◽  
Image Completion ◽  
Image Patches ◽  
Real Time Applications ◽  
Surrounding Areas ◽  
Straight Lines ◽  
Better Than

Image completion techniques can be used to repair unknown image regions. However, existing techniques are too slow for real-time applications. In this paper, an image completion technique based on randomized correspondence is presented to accelerate the completing process. Some good patch matches are found via random sampling and propagated to surrounding areas. Approximate nearest neighbor matches between image patches can be found in real-time. For images with strong structure, straight lines or curves across unknown regions can be manually specified to preserve the important structures. In such case, search is only performed on specified lines or curves. Finally, the remaining unknown regions can be filled using randomized correspondence with structural constraint. The experiments show that the quality and speed of presented technique are much better than that of existing methods.

scholarly journals Real-time detection of planar regions in unorganized point clouds

2015 ◽  
Vol 48 (6) ◽  
pp. 2043-2053 ◽  
Author(s):  
Frederico A. Limberger ◽  
Manuel M. Oliveira
Keyword(s):  
Real Time ◽  
Point Clouds ◽  
Real Time Detection ◽  
Planar Regions

scholarly journals BMP-RAP: Branching Multithreaded Pipeline for Real-time Applications with Pooled Resources

2019 ◽  
Author(s):  
Michael Sherer ◽  
Ebin Scaria
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Directed Graph ◽  
Graph Structure ◽  
Performance Gain ◽  
Filter Structure ◽  
Computer Vision Systems ◽  
Real Time Applications ◽  
Time Critical ◽  
Better Than

Many programs have a fixed directed graph structure in the way they are processed. In particular, computer vision systems often employ this kind of pipe-and-filter structure. It is desirable to take advantage of the inherent parallelism in such a system. Additionally, such systems need to run in real-time for robotics applications. In such applications, robotic platforms must make time-critical decisions, and so any additional performance gain would be beneficial. To further improve on this, the platform may need to make the best decision it can by a given time, so that newer data can be processed. Thus, having a timeout that would return a good result may be better than operating on outdated information.

scholarly journals Curvature Adaptive 3D Scanning Transformation Calculation

2018 ◽  
Vol 62 (4) ◽  
pp. 107-116
Author(s):  
Adrián Mezei ◽  
Tibor Kovács
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Scanning ◽  
Laser Line ◽  
3D Scanner ◽  
Three Dimensional Objects ◽  
Laser Scanners ◽  
Set Up

Three-dimensional objects can be scanned by 3D laser scanners that use active triangulation. These scanners create three-dimensional point clouds from the scanned objects. The laser line is identified in the images, which are captured at given transformations by the camera, and the point cloud can be calculated from these. The hardest challenge is to construct these transformations so that most of the surface can be captured. The result of a scanning may have missing parts because either not the best transformations were used or because some parts of the object cannot be scanned. Based on the results of the previous scans, a better transformation plan can be created, with which the next scan can be performed. In this paper, a method is proposed for transforming a special 3D scanner into a position from where the scanned point can be seen from an ideal angle. A method is described for estimating this transformation in real-time, so these can be calculated for every point of a previous scan to set up a next improved scan.

Maintaining Consistency Over A Network in Real-Time Applications

1989 ◽  
Author(s):  
Insup Lee ◽  
Susan Davidson ◽  
Victor Wolfe
Keyword(s):  
Real Time ◽  
Real Time Applications

Peak-Power-Aware Energy Management for Periodic Real-Time Applications

2020 ◽  
Vol 39 (4) ◽  
pp. 779-788 ◽  
Author(s):  
Mohsen Ansari ◽  
Amir Yeganeh-Khaksar ◽  
Sepideh Safari ◽  
Alireza Ejlali
Keyword(s):  
Real Time ◽  
Energy Management ◽  
Peak Power ◽  
Real Time Applications

scholarly journals Cloth simulation-based construction of pit-free canopy height models from airborne LiDAR data

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Wuming Zhang ◽  
Shangshu Cai ◽  
Xinlian Liang ◽  
Jie Shao ◽  
Ronghai Hu ◽  
...  
Keyword(s):  
Tree Height ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Canopy Height ◽  
Lidar Data ◽  
Cloth Simulation ◽  
Simulation Based ◽  
Maximum Tree Height ◽  
Airborne Lidar Data ◽  
Better Than

Abstract Background The universal occurrence of randomly distributed dark holes (i.e., data pits appearing within the tree crown) in LiDAR-derived canopy height models (CHMs) negatively affects the accuracy of extracted forest inventory parameters. Methods We develop an algorithm based on cloth simulation for constructing a pit-free CHM. Results The proposed algorithm effectively fills data pits of various sizes whilst preserving canopy details. Our pit-free CHMs derived from point clouds at different proportions of data pits are remarkably better than those constructed using other algorithms, as evidenced by the lowest average root mean square error (0.4981 m) between the reference CHMs and the constructed pit-free CHMs. Moreover, our pit-free CHMs show the best performance overall in terms of maximum tree height estimation (average bias = 0.9674 m). Conclusion The proposed algorithm can be adopted when working with different quality LiDAR data and shows high potential in forestry applications.

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