Cloth Simulation Research Articles

Precise 3D extraction of building roofs by fusion of UAV-based thermal and visible images

ABSTRACT Thermography is an efficient way of detecting the thermal problems of the roof as a major part of a building’s energy dissipation. Thermal images have a low spatial resolution, making it a challenge to produce a three-dimensional thermal model using aerial images. This paper proposes a combination of thermal and visible point clouds to generate a higher-resolution thermal point cloud from roofs of buildings. For this purpose, after obtaining the internal orientation parameters through camera calibration, visible and thermal point clouds were generated and then registered to each other using ground control points. The roofs of buildings were then extracted from the visible point cloud in four steps. First ground points were removed using cloth simulation filter (CSF), and then vegetation points were eliminated by applying entropy and red-green-blue vegetation index (RGBVI). Geometric features and a segmentation step were considered to filter roofs from other parts. Finally, by combining visible and thermal point clouds, the generated point had a high spatial resolution along with thermal information. In the achieved results, the thermal camera calibration was performed with an accuracy of 0.31 pixels, and the thermal and visible point clouds were registered with an absolute distance of < 0.3 m. The experimental results showed an accuracy of 18 cm for automated extraction of building roofs and 0.6 pixel for production of a high-resolution thermal point cloud, which was five times the density of the primary thermal point cloud and free from distortions.

Vegetation Filtering of a Steep Rugged Terrain: The Performance of Standard Algorithms and a Newly Proposed Workflow on an Example of a Railway Ledge

Point clouds derived using structure from motion (SfM) algorithms from unmanned aerial vehicles (UAVs) are increasingly used in civil engineering practice. This includes areas such as (vegetated) rock outcrops or faces above linear constructions (e.g., railways) where accurate terrain identification, i.e., ground filtering, is highly difficult but, at the same time, important for safety management. In this paper, we evaluated the performance of standard geometrical ground filtering algorithms (a progressive morphological filter (PMF), a simple morphological filter (SMRF) or a cloth simulation filter (CSF)) and a structural filter, CANUPO (CAractérisation de NUages de POints), for ground identification in a point cloud derived by SfM from UAV imagery in such an area (a railway ledge and the adjacent rock face). The performance was evaluated both in the original position and after levelling the point cloud (its transformation into the horizontal plane). The poor results of geometrical filters (total errors of approximately 6–60% with PMF performing the worst) and a mediocre result of CANUPO (approximately 4%) led us to combine these complementary approaches, yielding total errors of 1.2% (CANUPO+SMRF) and 0.9% (CANUPO+CSF). This new technique could represent an excellent solution for ground filtering of high-density point clouds of such steep vegetated areas that can be well-used, for example, in civil engineering practice.

An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

GPU-based simulation of cloth wrinkles at submillimeter levels

In this paper, we study physics-based cloth simulation in a very high resolution setting, presumably at submillimeter levels with millions of vertices, to meet perceptual precision of our human eyes. State-of-the-art simulation techniques, mostly developed for unstructured triangular meshes, can hardly meet this demand due to their large computational costs and memory footprints. We argue that in a very high resolution, it is more plausible to use regular meshes with an underlying grid structure, which can be highly compatible with GPU acceleration like high-resolution images. Based on this idea, we formulate and solve the nonlinear optimization problem for simulating high-resolution wrinkles, by a fast block-based descent method with reduced memory accesses. We also investigate the development of the collision handling component in our system, whose performance benefits greatly from the grid structure. Finally, we explore various issues related to the applications of our system, including initialization for fast convergence and temporal coherence, gathering effects, inflation and stuffing models, and mesh simplification. We can treat our system as a quasistatic wrinkle synthesis tool, run it as a standalone dynamic simulator, or integrate it into a multi-resolution solver as an additional component. The experiment demonstrates the capability, efficiency and flexibility of our system in producing a variety of high-resolution wrinkles effects.

Integrated Laser Scanner Techniques to Produce High-Resolution DTM of Vegetated Territory

The paper presents the first part of a research project concerning the creation of 3D terrain models useful to understand landslide movements. Thus, it illustrates the creation process of a multi-source high-resolution Digital Terrain Model (DTM) in very dense vegetated areas obtained by integrating 3D data coming from three sources, starting from long and medium-range Terrestrial Laser Scanner up to a Backpack Indoor Mobile Mapping System. The point clouds are georeferenced by means of RKT GNSS points and automatically filtered using a Cloth Simulation Filter algorithm to separate points belonging to the ground. Those points are interpolated to produce the DTMs which are then mosaicked to obtain a unique multi-resolution DTM that plays a crucial role in the detection and identification of specific geological features otherwise visible. Standard deviation of residuals of the DTM varies from 0.105 m to 0.176 m for Z coordinate, from 0.065 m to 0.300 m for X and from 0.034 m to 0.175 m for Y. The area under investigation belongs to the Municipality of Piuro (SO) and includes both the town and surrounding valley. It was affected by a dramatic landslide in 1618 that destroyed the entire village. Numerous challenges have been faced, caused both by the characteristics of the area and the processed data. The complexity of the case study turns out to be an excellent test bench for the employed technologies, providing the opportunity to precisely identify the needed direction to obtain future promising results.

Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires a reasonably detailed mapping of the ditch profile to identify areas in need of excavation to remove long-term sediment accumulation. This study utilizes high-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) for mapping roadside ditches and performing hydrological analyses. The performance of alternative MLMS units, including an unmanned aerial vehicle, an unmanned ground vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system, is evaluated. Point clouds from all the MLMS units are in agreement within the ±3 cm range for solid surfaces and ±7 cm range for vegetated areas along the vertical direction. The portable backpack system that could be carried by a surveyor or mounted on a vehicle is found to be the most cost-effective method for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground-filtering approach—cloth simulation—is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from the LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data turned out to be very close to the highway cross slope design standards of 2% on driving lanes, 4% on shoulders, and a 6-by-1 slope for ditch lines.

Regularly striped preconditioner for implicit clothing simulation

Regularly striped preconditioner for implicit clothing simulation

Modeling and Simulation of Radiation-proof Clothing for Pregnant Women based on Finite Element Method

Modeling and Simulation of Radiation-proof Clothing for Pregnant Women based on Finite Element Method

Point cloud target detection and tracking algorithm based on K-means and Kalman

This paper is mainly about point cloud image and data processing, virtualization and visualization. An improved K-means iterative clustering algorithm is proposed. In addition, cloth simulation filtering algorithm is used to extract road surface information. On this basis, bilateral filtering and statistic filtering are used to smooth image denoising. Kalman filter is used to track and predict point cloud targets. Using the variance of innovation sequence, the noise variance and observation noise variance can be estimated and corrected gradually in the process of system calculation. The error is relatively small, and the accuracy and reliability are improved. For disordered, occluded, sparsity and noisy, complex terrain image, how to improve the denoising effect, the accuracy and effectiveness of target extraction and tracking is the focus of this paper.

A New Challenge: Detection of Small-Scale Falling Rocks on Transportation Roads in Open-Pit Mines

In transportation at open-pit mines, rocks dropped as a mining truck is driven will wear out the tires of the vehicle, thus increasing the mining cost. In the case of autonomous vehicles, the vehicle must automatically detect rocks on the transportation roads during the driving process. This will be a new challenge: rough road, rocks of small size and irregular shape, long detection distance, etc. This paper presents a detection method based on light detection and ranging (lidar). It includes two stages: (1) using the modified cloth simulation method to filter out the ground points; (2) using the regional growth method based on grid division to cluster non-ground points. Experimental results show that the method can detect rocks with a size of 20–30 cm at a distance of 40 m in front of the vehicle, and it takes only 0.3 s on an ordinary personal computer (PC). This method is easy to understand, and it has fewer parameters to be adjusted. Therefore, it is a better method for detecting small, irregular obstacles on a low-speed, unstructured and rough road.

Canopy Parameter Estimation of Citrus grandis var. Longanyou Based on LiDAR 3D Point Clouds

The characteristic parameters of Citrus grandis var. Longanyou canopies are important when measuring yield and spraying pesticides. However, the feasibility of the canopy reconstruction method based on point clouds has not been confirmed with these canopies. Therefore, LiDAR point cloud data for C. grandis var. Longanyou were obtained to facilitate the management of groves of this species. Then, a cloth simulation filter and European clustering algorithm were used to realize individual canopy extraction. After calculating canopy height and width, canopy reconstruction and volume calculation were realized using six approaches: by a manual method and using five algorithms based on point clouds (convex hull, CH; convex hull by slices; voxel-based, VB; alpha-shape, AS; alpha-shape by slices, ASBS). ASBS is an innovative algorithm that combines AS with slices optimization, and can best approximate the actual canopy shape. Moreover, the CH algorithm had the shortest run time, and the R2 values of VCH, VVB, VAS, and VASBS algorithms were above 0.87. The volume with the highest accuracy was obtained from the ASBS algorithm, and the CH algorithm had the shortest computation time. In addition, a theoretical but preliminarily system suitable for the calculation of the canopy volume of C. grandis var. Longanyou was developed, which provides a theoretical reference for the efficient and accurate realization of future functional modules such as accurate plant protection, orchard obstacle avoidance, and biomass estimation.

Improving the estimation of canopy cover from UAV-LiDAR data using a pit-free CHM-based method

ABSTRACT Accurate and rapid estimation of canopy cover (CC) is crucial for many ecological and environmental models and for forest management. Unmanned aerial vehicle-light detecting and ranging (UAV-LiDAR) systems represent a promising tool for CC estimation due to their high mobility, low cost, and high point density. However, the CC values from UAV-LiDAR point clouds may be underestimated due to the presence of large quantities of within-crown gaps. To alleviate the negative effects of within-crown gaps, we proposed a pit-free CHM-based method for estimating CC, in which a cloth simulation method was used to fill the within-crown gaps. To evaluate the effect of CC values and within-crown gap proportions on the proposed method, the performance of the proposed method was tested on 18 samples with different CC values (40−70%) and 6 samples with different within-crown gap proportions (10−60%). The results showed that the CC accuracy of the proposed method was higher than that of the method without filling within-crown gaps (R 2 = 0.99 vs 0.98; RMSE = 1.49% vs 2.2%). The proposed method was insensitive to within-crown gap proportions, although the CC accuracy decreased slightly with the increase in within-crown gap proportions.

Modeling and Analysis of Human Heat transfer in Cryogenic Protective clothing based on the principle of Heat transfer

In this paper, the simulation of low temperature protective clothing with phase change material is taken as the research object, and the model based on heat conduction equation is established through Fourie heat transfer law, various heat transfer modes and three kinds of boundary conditions. Based on the principle of heat transfer, two kinds of heat transfer modes, heat conduction and heat convection, are mainly considered in this paper. First of all, through the analysis of the heat transfer mechanism of low temperature protective composites in low temperature environment, the boundary conditions of thermal insulation layer and fabric layer are the third kind of boundary conditions, which can be regarded as a convective heat transfer process. Then the partial differential extraordinary model can be established according to the boundary conditions. The interior of the thermal insulation layer, the functional layer and the fabric layer are heat conduction, which can be solved by establishing a model according to Fourier law. Then this paper simulates the above two models and uses the implicit post-difference scheme (Crank method) to solve them in parts.

Automatic detection of power transmission lines and risky object locations using UAV LiDAR data

The periodic monitoring of energy lines to lessen the impacts of threats and to destroy the potential risks against the power transmission lines (PTL) is highly important. The risks can involve natural causes (vegetation, landslides, trees, avalanches, storms, etc.) on the one hand and the human factor (constructions and buildings breaking the safety distance, dumping the excavated material, theft, etc.) on the other hand. In this study, an algorithm, which can automatically detect PTLs’ wires and pylons using the UAV LiDAR data, is developed. Specific safety distances are also spatially analyzed and the existence of risky ground objects is examined with the help of the detected PTLs. In the newly developed algorithm, ground points are located and the low object points in vertical distance to these ground points are eliminated using the cloth simulation filtering (CSF) method. The remaining point cloud is separated into voxels of 5×5×5m in size. In the search of 26 neighbor voxels, starting from automatically determined seed voxel, final detection of wire and pylons has been determined by the algorithm of “concave hull” after their straight slopes which are fitted by height values variant and RANSAC were analyzed. The accuracy value for wires was 97.13%, the integrity value was 97.36%, the quality value was 94.63%, for the pylons the accuracy value was 70.25%, the integrity value was 94.24% and the quality value was 67.36% in the algorithm developed. Periodic applications based on the proposed approach will make it easy to monitor and maintain PTLs components (wires and pylons) without time-consuming field works.

Seed point set-based building roof extraction from airborne LiDAR point clouds using a top-down strategy

Building roof extraction from airborne laser scanning point clouds is significant for building modeling. The common method adopts a bottom-up strategy which requires a ground filtering process first, and the subsequent process of region growing based on a single seed point easily causes oversegmentation problem. This paper proposes a novel method to extract roofs. A top-down strategy based on cloth simulation is first used to detect seed point sets with semantic information; then, the roof seed points are extracted instead of a single seed point for region-growing segmentation. The proposed method is validated by three point cloud datasets that contain different types of roof and building footprints. The results show that the top-down strategy directly extracts roof seed point sets, most roofs are extracted by the region-growing algorithm based on the seed point set, and the total errors of roof extraction in the test areas are 0.65%, 1.07%, and 1.45%. The proposed method simplifies the workflow of roof extraction, reduces oversegmentation, and determines roofs in advance based on the semantic seed point set, which suggests a practical solution for rapid roof extraction.

Automatic extraction of trees by using multiple return properties of the lidar point cloud

Airborne laser scanning has been a valuable tool for forestry applications since it began to be used commercially. Thanks to the high 3D resolution provided by the Light Detection and Ranging (LiDAR) point cloud, it has provided great convenience in complex 3D modeling processes needed for forestry applications such as forest inventory, forest management, determination of carbon stocks and the characterization of biodiversity. LiDAR data provides a new dimension in forestry applications with its high 3D resolution and multiple return characteristics. The extraction of woodland areas from the LiDAR point cloud has great importance for automating the determination of tree heights, species and stand frequency which will be used for generating canopy height models (CHM). In this study, woodland areas in the urban scene were automatically extracted by using the multiple return properties of the LiDAR point cloud. The proposed approach consists of three major steps namely pre-processing, parameter calculation and k-d tree search for trees which were implemented in MATLAB. In the first step, multiple return points have been identified from the LiDAR point cloud, which will be then used to determine possible tree locations. Then, by using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, neighborhood relations among the multi return points which were extracted from the initial point cloud data, were formed and a rule-based filter was applied by taking advantage of neighborhood relations. In addition, the initial point cloud was filtered with the Cloth Simulation Filtering (CSF) algorithm to separate ground and non-ground points where non-ground points used to extract trees. In the second step, non-vegetation points were removed by applying a threshold based on the change of curvature and planarity parameters, which are derived from the filtered non–ground point cloud. In the last step, in order to extract trees, a k-d tree structure was created from the filtered non-ground points to find nearest neighbors of each multi return point within a given diameter in the k-d tree structure. In order to evaluate the accuracy of the approach, the extracted boundaries were compared with the manually digitized woodland boundaries from the true orthophoto of the study area using correctness, completeness and quality metrics.

Automated ground filtering of LiDAR and UAS point clouds with metaheuristics

The ground filtering is essential for the extraction of the topography of the bare Earth surface. Various ground filtering methods have been developed, especially in the last three decades. The main disadvantage of the ground filtering methods is that their performances are highly dependent on some user-defined parameter values. Hence, the analysts usually have to try a large number of parameter values until the optimum ground filtering result is achieved, which is neither practical nor time-efficient, especially for topographies with abrupt elevation changes. In addition, inappropriate parameter values may lead to the misclassification of the points that belong to the ground surface and to the above-ground objects. In cases where the analyst is not experienced in ground filtering, classification errors are expected to increase significantly. This reveals the necessity of an automated ground filtering strategy to avoid the user intervention for minimum classification errors. Hence, this study proposed to automate one of the most successful ground filtering methods, cloth simulation filtering (CSF), through algorithm-specific parameter-free metaheuristic optimization algorithms Grey Wolf Optimizer (GWO) and Jaya. The performances of the proposed GWO-based CSF (GWO-CSF) and Jaya-based CSF (Jaya-CSF) methods were tested on three LiDAR and two UAS point clouds. The results of the GWO-CSF and Jaya-CSF methods were qualitatively and quantitatively compared against those of the widely-used ground filtering methods progressive morphological 2D (PM2D), maximum local slope (MLS), elevation threshold with expand window (ETEW), multi-scale curvature classification (MCC), Boise Centre Aerospace Laboratory LiDAR (BCAL), gLiDAR, progressive triangulated irregular network densification (PTD) and standard CSF in five test sites. The performance evaluations revealed that the proposed GWO-CSF and Jaya-CSF methods did not only outperform the standard CSF, but also the other filtering methods used. The GWO-CSF and Jaya-CSF methods were also found to achieve the best balance between the omission and commission errors. It was also concluded that the GWO-CSF and Jaya-CSF methods did not only perform well on gentle slopes, but also on sloping terrains with various large complex-shaped above ground objects. Another important conclusion is that the GWO-CSF and Jaya-CSF methods presented a very high filtering performance on both LiDAR and UAS point clouds. The proposed methods managed to automate the filtering process, minimizing the filtering errors.

Segmentation of Seabed Points from Airborne Bathymetric LiDAR Point Clouds Using Cloth Simulation Filtering Algorithm

Segmentation of Seabed Points from Airborne Bathymetric LiDAR Point Clouds Using Cloth Simulation Filtering Algorithm

Towards Clothes Hanging via Cloth Simulation and Deep Convolutional Networks

Towards Clothes Hanging via Cloth Simulation and Deep Convolutional Networks

Method for determining cloth simulation filtering threshold value based on curvature value of fitting curve

Cloth Simulation Filtering (CSF) is an algorithm that is effectively applied to mobile 3D laser point cloud filtering. The classification threshold in cloth simulation is a key parameter for separa...