scholarly journals Multisource Point Clouds, Point Simplification and Surface Reconstruction

2019 ◽  
Vol 11 (22) ◽  
pp. 2659 ◽  
Author(s):  
Zhu ◽  
Kukko ◽  
Virtanen ◽  
Hyyppä ◽  
Kaartinen ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Surface Reconstruction ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Algebraic Point ◽  
Point Reduction ◽  
Alpha Shapes ◽  
Reconstruction Methods ◽  
Point Set ◽  
Different Levels

As data acquisition technology continues to advance, the improvement and upgrade of the algorithms for surface reconstruction are required. In this paper, we utilized multiple terrestrial Light Detection And Ranging (Lidar) systems to acquire point clouds with different levels of complexity, namely dynamic and rigid targets for surface reconstruction. We propose a robust and effective method to obtain simplified and uniform resample points for surface reconstruction. The method was evaluated. A point reduction of up to 99.371% with a standard deviation of 0.2 cm was achieved. In addition, well-known surface reconstruction methods, i.e., Alpha shapes, Screened Poisson reconstruction (SPR), the Crust, and Algebraic point set surfaces (APSS Marching Cubes), were utilized for object reconstruction. We evaluated the benefits in exploiting simplified and uniform points, as well as different density points, for surface reconstruction. These reconstruction methods and their capacities in handling data imperfections were analyzed and discussed. The findings are that i) the capacity of surface reconstruction in dealing with diverse objects needs to be improved; ii) when the number of points reaches the level of millions (e.g., approximately five million points in our data), point simplification is necessary, as otherwise, the reconstruction methods might fail; iii) for some reconstruction methods, the number of input points is proportional to the number of output meshes; but a few methods are in the opposite; iv) all reconstruction methods are beneficial from the reduction of running time; and v) a balance between the geometric details and the level of smoothing is needed. Some methods produce detailed and accurate geometry, but their capacity to deal with data imperfection is poor, while some other methods exhibit the opposite characteristics.

scholarly journals Computational Geometry-Based Surface Reconstruction for Volume Estimation: A Case Study on Magnitude-Frequency Relations for a LiDAR-Derived Rockfall Inventory

2021 ◽  
Vol 10 (3) ◽  
pp. 157
Author(s):  
Paul-Mark DiFrancesco ◽  
David A. Bonneau ◽  
D. Jean Hutchinson
Keyword(s):  
Computational Geometry ◽  
Surface Reconstruction ◽  
Power Law ◽  
Point Cloud ◽  
Point Clouds ◽  
Volume Estimation ◽  
Estimation Methods ◽  
Small Scale ◽  
Reconstruction Methods ◽  
3D Volume

Key to the quantification of rockfall hazard is an understanding of its magnitude-frequency behaviour. Remote sensing has allowed for the accurate observation of rockfall activity, with methods being developed for digitally assembling the monitored occurrences into a rockfall database. A prevalent challenge is the quantification of rockfall volume, whilst fully considering the 3D information stored in each of the extracted rockfall point clouds. Surface reconstruction is utilized to construct a 3D digital surface representation, allowing for an estimation of the volume of space that a point cloud occupies. Given various point cloud imperfections, it is difficult for methods to generate digital surface representations of rockfall with detailed geometry and correct topology. In this study, we tested four different computational geometry-based surface reconstruction methods on a database comprised of 3668 rockfalls. The database was derived from a 5-year LiDAR monitoring campaign of an active rock slope in interior British Columbia, Canada. Each method resulted in a different magnitude-frequency distribution of rockfall. The implications of 3D volume estimation were demonstrated utilizing surface mesh visualization, cumulative magnitude-frequency plots, power-law fitting, and projected annual frequencies of rockfall occurrence. The 3D volume estimation methods caused a notable shift in the magnitude-frequency relations, while the power-law scaling parameters remained relatively similar. We determined that the optimal 3D volume calculation approach is a hybrid methodology comprised of the Power Crust reconstruction and the Alpha Solid reconstruction. The Alpha Solid approach is to be used on small-scale point clouds, characterized with high curvatures relative to their sampling density, which challenge the Power Crust sampling assumptions.

scholarly journals Improving the Accuracy of Automatic Reconstruction of 3D Complex Buildings Models from Airborne Lidar Point Clouds

2020 ◽  
Vol 12 (10) ◽  
pp. 1643 ◽  
Author(s):  
Marek Kulawiak ◽  
Zbigniew Lubniewski
Keyword(s):  
Surface Reconstruction ◽  
Spatial Data ◽  
Measurement Data ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Lidar ◽  
Reconstruction Method ◽  
Cloud Data ◽  
Reconstruction Methods

Due to high requirements of variety of 3D spatial data applications with respect to data amount and quality, automatized, efficient and reliable data acquisition and preprocessing methods are needed. The use of photogrammetry techniques—as well as the light detection and ranging (LiDAR) automatic scanners—are among attractive solutions. However, measurement data are in the form of unorganized point clouds, usually requiring transformation to higher order 3D models based on polygons or polyhedral surfaces, which is not a trivial process. The study presents a newly developed algorithm for correcting 3D point cloud data from airborne LiDAR surveys of regular 3D buildings. The proposed approach assumes the application of a sequence of operations resulting in 3D rasterization, i.e., creation and processing of a 3D regular grid representation of an object, prior to applying a regular Poisson surface reconstruction method. In order to verify the accuracy and quality of reconstructed objects for quantitative comparison with the obtained 3D models, high-quality ground truth models were used in the form of the meshes constructed from photogrammetric measurements and manually made using buildings architectural plans. The presented results show that applying the proposed algorithm positively influences the quality of the results and can be used in combination with existing surface reconstruction methods in order to generate more detailed 3D models from LiDAR scanning.

scholarly journals 3D Reconstruction using Kinect Sensor and Parallel Processing on 3D Graphics Processing Unit

2013 ◽  
Vol 3 (1-2) ◽  
Author(s):  
Thuong Le-Tien ◽  
Marie Luong ◽  
Thai Phu Ho ◽  
Viet Dai Tran
Keyword(s):  
Graphics Processing Unit ◽  
Point Clouds ◽  
Microsoft Kinect ◽  
Alignment Algorithm ◽  
Processing Unit ◽  
Initial Alignment ◽  
Depth Cameras ◽  
3D Point Clouds ◽  
Alpha Shapes ◽  
Reconstruction Methods

One of depth cameras such as the Microsoft Kinect is much cheaper than conventional 3D scanning devices, thus it can be acquired for everyday users easily. However, the depth data captured by Kinect over a certain distance is of low quality. In this work, we implement a set of algorithms allowing users to capture 3D surfaces by using the handheld Kinect. As a classic alignment algorithm such as the Iterative Closest Point (ICP) does not show efficacy in aligning point clouds that have limited overlapped regions, another coarse alignment using the Sample Consensus Initial Alignment (SAC-IA) is incorporated in to the registration process in order to ameliorate 3D point clouds’ fitness. Two robust reconstruction methods namely the Alpha Shapes and the Grid Projection are also implemented to reconstruct 3D surface from registered point clouds. The experimental results have shown the efficiency and applicability of of our blueprint. The constructed system obtains acceptable results in a few minutes with a low price device, thus it may practically be an useful approach for avatar generations or online shoppings.

scholarly journals Self-Supervised Point Set Local Descriptors for Point Cloud Registration

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 486
Author(s):  
Yijun Yuan ◽  
Dorit Borrmann ◽  
Jiawei Hou ◽  
Yuexin Ma ◽  
Andreas Nüchter ◽  
...  
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Point Clouds ◽  
Point Cloud Registration ◽  
Local Descriptor ◽  
Current State ◽  
Manual Selection ◽  
Point Set ◽  
Different Levels ◽  
Selection Of

Descriptors play an important role in point cloud registration. The current state-of-the-art resorts to the high regression capability of deep learning. However, recent deep learning-based descriptors require different levels of annotation and selection of patches, which make the model hard to migrate to new scenarios. In this work, we learn local registration descriptors for point clouds in a self-supervised manner. In each iteration of the training, the input of the network is merely one unlabeled point cloud. Thus, the whole training requires no manual annotation and manual selection of patches. In addition, we propose to involve keypoint sampling into the pipeline, which further improves the performance of our model. Our experiments demonstrate the capability of our self-supervised local descriptor to achieve even better performance than the supervised model, while being easier to train and requiring no data labeling.

scholarly journals LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2263
Author(s):  
Haileleol Tibebu ◽  
Jamie Roche ◽  
Varuna De Silva ◽  
Ahmet Kondoz
Keyword(s):  
Laser Scanner ◽  
Point Clouds ◽  
Light Detection And Ranging ◽  
Test Results ◽  
New Approach ◽  
Occupancy Grid ◽  
Laser Scanners ◽  
Novel Method ◽  
Occupancy Grid Mapping ◽  
Cartesian Coordinate

Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

Research on the Method of Class_A Surface Reconstruction in Automotive Body External Panel Design

2011 ◽  
Vol 291-294 ◽  
pp. 2229-2232
Author(s):  
Ya Bin Cao
Keyword(s):  
Surface Quality ◽  
Surface Reconstruction ◽  
Point Clouds ◽  
Traditional Methods ◽  
Surface Design ◽  
Automotive Body ◽  
Reconstruction Efficiency ◽  
Low Efficiency ◽  
Complicated Surface ◽  
Panel Design

Class_A surface reconstruction is a key part in the design of automotive body external panel. Traditional methods of Class_A surface reconstruction have some disadvantages such as low efficiency, bad flexibility and low surface quality in complicated surface reconstruction. In this paper, a method of Class_A surface reconstruction from point clouds based on NURBS patch was presented, which made surface design more flexible and direct, besides, the reconstruction efficiency and surface quality were improved.

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