scholarly journals An Elevation Ambiguity Resolution Method Based on Segmentation and Reorganization of TomoSAR Point Cloud in 3D Mountain Reconstruction

2021 ◽  
Vol 13 (24) ◽  
pp. 5118
Author(s):  
Xiaowan Li ◽  
Fubo Zhang ◽  
Yanlei Li ◽  
Qichang Guo ◽  
Yangliang Wan ◽  
...  
Keyword(s):  
Ambiguity Resolution ◽  
Point Cloud ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
High Mountain ◽  
Real Point ◽  
Resolution Method ◽  
Mountain Areas ◽  
External Data

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method.

scholarly journals Increasing Spatio-Temporal Resolution for Monitoring Alpine Solifluction Using Terrestrial Laser Scanners and 3D Vector Fields

2021 ◽  
Vol 13 (6) ◽  
pp. 1192
Author(s):  
Christoph Holst ◽  
Jannik Janßen ◽  
Berit Schmitz ◽  
Martin Blome ◽  
Malte Dercks ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Point Cloud ◽  
Vector Fields ◽  
Feature Detection ◽  
Water Saturation ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
The Arctic ◽  
High Mountain ◽  
Mountain Areas

This article investigates the usage of terrestrial laser scanner (TLS) point clouds for monitoring the gradual movements of soil masses due to freeze–thaw activity and water saturation, commonly referred to as solifluction. Solifluction is a geomorphic process which is characteristic for hillslopes in (high-)mountain areas, primarily alpine periglacial areas and the arctic. The movement can reach millimetre-to-centimetre per year velocities, remaining well below the typical displacement mangitudes of other frequently monitored natural objects, such as landslides and glaciers. Hence, a better understanding of solifluction processes requires increased spatial and temporal resolution with relatively high measurement accuracy. To that end, we developed a workflow for TLS point cloud processing, providing a 3D vector field that can capture soil mass displacement due to solifluction with high fidelity. This is based on the common image-processing techniques of feature detection and tracking. The developed workflow is tested on a study area placed in Hohe Tauern range of the Austrian Alps with a prominent assemblage of solifluction lobes. The derived displacements were compared with the established geomonitoring approach with total station and signalized markers and point cloud deformation monitoring approaches. The comparison indicated that the achieved results were in the same accuracy range as the established methods, with an advantage of notably higher spatial resolution. This improvement allowed for new insights considering the solifluction processes.

Kinect Depth Data Segmentation Based on Gauss Mixture Model Clustering

2013 ◽  
Vol 760-762 ◽  
pp. 1556-1561
Author(s):  
Ting Wei Du ◽  
Bo Liu
Keyword(s):  
Mixture Model ◽  
Point Cloud ◽  
Three Dimensional ◽  
Image Data ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
Depth Image ◽  
Cloud Data ◽  
Mixture Model Clustering ◽  
Gauss Mixture

Indoor scene understanding based on the depth image data is a cutting-edge issue in the field of three-dimensional computer vision. Taking the layout characteristics of the indoor scenes and more plane features in these scenes into account, this paper presents a depth image segmentation method based on Gauss Mixture Model clustering. First, transform the Kinect depth image data into point cloud which is in the form of discrete three-dimensional point data, and denoise and down-sample the point cloud data; second, calculate the point normal of all points in the entire point cloud, then cluster the entire normal using Gaussian Mixture Model, and finally implement the entire point clouds segmentation by RANSAC algorithm. Experimental results show that the divided regions have obvious boundaries and segmentation quality is above normal, and lay a good foundation for object recognition.

scholarly journals Point cloud clustering and outlier detection based on spatial neighbor connected region labeling

2020 ◽  
pp. 002029402091986
Author(s):  
Xiaocui Yuan ◽  
Huawei Chen ◽  
Baoling Liu
Keyword(s):  
Outlier Detection ◽  
Point Cloud ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Point Clouds ◽  
Detection Methods ◽  
Connected Region ◽  
Computational Time ◽  
Region Labeling

Clustering analysis is one of the most important techniques in point cloud processing, such as registration, segmentation, and outlier detection. However, most of the existing clustering algorithms exhibit a low computational efficiency with the high demand for computational resources, especially for large data processing. Sometimes, clusters and outliers are inseparable, especially for those point clouds with outliers. Most of the cluster-based algorithms can well identify cluster outliers but sparse outliers. We develop a novel clustering method, called spatial neighborhood connected region labeling. The method defines spatial connectivity criterion, finds points connections based on the connectivity criterion among the k-nearest neighborhood region and classifies connected points to the same cluster. Our method can accurately and quickly classify datasets using only one parameter k. Comparing with K-means, hierarchical clustering and density-based spatial clustering of applications with noise methods, our method provides better accuracy using less computational time for data clustering. For applications in the outlier detection of the point cloud, our method can identify not only cluster outliers, but also sparse outliers. More accurate detection results are achieved compared to the state-of-art outlier detection methods, such as local outlier factor and density-based spatial clustering of applications with noise.

scholarly journals A Novel Recursive Non-Parametric DBSCAN Algorithm for 3D Data Analysis with an Application in Rockfall Detection

2021 ◽  
Vol 16 (4) ◽  
pp. 579-587
Author(s):  
Pitisit Dillon ◽  
Pakinee Aimmanee ◽  
Akihiko Wakai ◽  
Go Sato ◽  
Hoang Viet Hung ◽  
...  
Keyword(s):  
Point Cloud ◽  
Crack Detection ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Divide And Conquer ◽  
Dbscan Algorithm ◽  
3D Data ◽  
Data Points ◽  
Clustering Data ◽  
Better Than

The density-based spatial clustering of applications with noise (DBSCAN) algorithm is a well-known algorithm for spatial-clustering data point clouds. It can be applied to many applications, such as crack detection, rockfall detection, and glacier movement detection. Traditional DBSCAN requires two predefined parameters. Suitable values of these parameters depend upon the distribution of the input point cloud. Therefore, estimating these parameters is challenging. This paper proposed a new version of DBSCAN that can automatically customize the parameters. The proposed method consists of two processes: initial parameter estimation based on grid analysis and DBSCAN based on the divide-and-conquer (DC-DBSCAN) approach, which repeatedly performs DBSCAN on each cluster separately and recursively. To verify the proposed method, we applied it to a 3D point cloud dataset that was used to analyze rockfall events at the Puiggcercos cliff, Spain. The total number of data points used in this study was 15,567. The experimental results show that the proposed method is better than the traditional DBSCAN in terms of purity and NMI scores. The purity scores of the proposed method and the traditional DBSCAN method were 96.22% and 91.09%, respectively. The NMI scores of the proposed method and the traditional DBSCAN method are 0.78 and 0.49, respectively. Also, it can detect events that traditional DBSCAN cannot detect.

scholarly journals IMAGE TO POINT CLOUD TRANSLATION USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORK FOR AIRBORNE LIDAR DATA

2021 ◽  
Vol V-2-2021 ◽  
pp. 169-174
Author(s):  
T. Shinohara ◽  
H. Xiu ◽  
M. Matsuoka
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Real Point ◽  
3D Point Cloud ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
3D Point Clouds ◽  
Latent Features

Abstract. This study introduces a novel image to a 3D point-cloud translation method with a conditional generative adversarial network that creates a large-scale 3D point cloud. This can generate supervised point clouds observed via airborne LiDAR from aerial images. The network is composed of an encoder to produce latent features of input images, generator to translate latent features to fake point clouds, and discriminator to classify false or real point clouds. The encoder is a pre-trained ResNet; to overcome the difficulty of generating 3D point clouds in an outdoor scene, we use a FoldingNet with features from ResNet. After a fixed number of iterations, our generator can produce fake point clouds that correspond to the input image. Experimental results show that our network can learn and generate certain point clouds using the data from the 2018 IEEE GRSS Data Fusion Contest.

scholarly journals Full 4D Change Analysis of Topographic Point Cloud Time Series using Kalman Filtering

2022 ◽  
Author(s):  
Lukas Winiwarter ◽  
Katharina Anders ◽  
Daniel Schröder ◽  
Bernhard Höfle
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
High Mountain ◽  
Change Analysis ◽  
Area Of Interest ◽  
Rainy Weather

Abstract. 4D topographic point cloud data contain information on surface change processes and their spatial and temporal characteristics, such as the duration, location, and extent of mass movements, e.g., rockfalls or debris flows. To automatically extract and analyse change and activity patterns from this data, methods considering the spatial and temporal properties are required. The commonly used M3C2 point cloud distance reduces uncertainty through spatial averaging for bitemporal analysis. To extend this concept into the full 4D domain, we use a Kalman filter for point cloud change analysis. The filter incorporates M3C2 distances together with uncertainties obtained through error propagation as Bayesian priors in a dynamic model. The Kalman filter yields a smoothed estimate of the change time series for each spatial location, again associated with an uncertainty. Through the temporal smoothing, the Kalman filter uncertainty is, in general, lower than the individual bitemporal uncertainties, which therefore allows detection of more change as significant. In our example time series of bi-hourly terrestrial laser scanning point clouds of around 6 days (71 epochs) showcasing a rockfall-affected high-mountain slope in Tyrol, Austria, we are able to almost double the number of points where change is deemed significant (from 14.9 % to 28.6 % of the area of interest). Since the Kalman filter allows interpolation and, under certain constraints, also extrapolation of the time series, the estimated change values can be temporally resampled. This can be critical for subsequent analyses that are unable to deal with missing data, as may be caused by, e.g., foggy or rainy weather conditions. We demonstrate two different clustering approaches, transforming the 4D data into 2D map visualisations that can be easily interpreted by analysts. By comparison to two state-of-the-art 4D point cloud change methods, we highlight the main advantage of our method to be the extraction of a smoothed best estimate time series for change at each location. A main disadvantage of not being able to detect spatially overlapping change objects in a single pass remains. In conclusion, the consideration of combined temporal and spatial data enables a notable reduction in the associated uncertainty of the quantified change value for each point in space and time, in turn allowing the extraction of more information from the 4D point cloud dataset.

scholarly journals A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2309
Author(s):  
Yifei Tian ◽  
Wei Song ◽  
Long Chen ◽  
Yunsick Sung ◽  
Jeonghoon Kwak ◽  
...  
Keyword(s):  
Point Cloud ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Obstacle Detection ◽  
Small Cells ◽  
Gpu Programming ◽  
Processing Unit ◽  
Height Distribution ◽  
Connected Component ◽  
Accurate Perception

Fast and accurate obstacle detection is essential for accurate perception of mobile vehicles’ environment. Because point clouds sensed by light detection and ranging (LiDAR) sensors are sparse and unstructured, traditional obstacle clustering on raw point clouds are inaccurate and time consuming. Thus, to achieve fast obstacle clustering in an unknown terrain, this paper proposes an elevation-reference connected component labeling (ER-CCL) algorithm using graphic processing unit (GPU) programing. LiDAR points are first projected onto a rasterized x–z plane so that sparse points are mapped into a series of regularly arranged small cells. Based on the height distribution of the LiDAR point, the ground cells are filtered out and a flag map is generated. Next, the ER-CCL algorithm is implemented on the label map generated from the flag map to mark individual clusters with unique labels. Finally, obstacle labeling results are inverse transformed from the x–z plane to 3D points to provide clustering results. For real-time 3D point cloud clustering, ER-CCL is accelerated by running it in parallel with the aid of GPU programming technology.

scholarly journals Hole Repairing Algorithm for 3D Point Cloud Model of Symmetrical Objects Grasped by the Manipulator

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7558
Author(s):  
Linyan Cui ◽  
Guolong Zhang ◽  
Jinshen Wang
Keyword(s):  
Point Cloud ◽  
Cloud Model ◽  
Engineering Application ◽  
Point Clouds ◽  
Real Point ◽  
3D Point Cloud ◽  
Virtual Camera ◽  
3D Point Clouds ◽  
Cloud Models ◽  
Point Cloud Model

For the engineering application of manipulator grasping objects, mechanical arm occlusion and limited imaging angle produce various holes in the reconstructed 3D point clouds of objects. Acquiring a complete point cloud model of the grasped object plays a very important role in the subsequent task planning of the manipulator. This paper proposes a method with which to automatically detect and repair the holes in the 3D point cloud model of symmetrical objects grasped by the manipulator. With the established virtual camera coordinate system and boundary detection, repair and classification of holes, the closed boundaries for the nested holes were detected and classified into two kinds, which correspond to the mechanical claw holes caused by mechanical arm occlusion and the missing surface produced by limited imaging angle. These two kinds of holes were repaired based on surface reconstruction and object symmetry. Experiments on simulated and real point cloud models demonstrate that our approach outperforms the other state-of-the-art 3D point cloud hole repair algorithms.

scholarly journals 3D Modelling of Interior Spaces: Learning the Language of Indoor Architecture

2014 ◽  
Vol XL-5 ◽  
pp. 321-326 ◽  
Author(s):  
K. Khoshelham ◽  
L. Díaz-Vilariño
Keyword(s):  
Point Cloud ◽  
Point Clouds ◽  
3D Models ◽  
3D Modelling ◽  
Shape Grammar ◽  
Real Point ◽  
Indoor Environments ◽  
Interior Spaces ◽  
Grammar Rules

3D models of indoor environments are important in many applications, but they usually exist only for newly constructed buildings. Automated approaches to modelling indoor environments from imagery and/or point clouds can make the process easier, faster and cheaper. We present an approach to 3D indoor modelling based on a shape grammar. We demonstrate that interior spaces can be modelled by iteratively placing, connecting and merging cuboid shapes. We also show that the parameters and sequence of grammar rules can be learned automatically from a point cloud. Experiments with simulated and real point clouds show promising results, and indicate the potential of the method in 3D modelling of large indoor environments.

Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution

2017 ◽  
Vol 11 (4) ◽  
pp. 657-665 ◽  
Author(s):  
Ryuji Miyazaki ◽  
Makoto Yamamoto ◽  
Koichi Harada ◽  
◽  
◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Region Growing ◽  
Point Clouds ◽  
Planar Structure ◽  
Scanning System ◽  
Normal Vector ◽  
Real Point ◽  
Planar Regions ◽  
Structure Extraction

We propose a line-based region growing method for extracting planar regions with precise boundaries from a point cloud with an anisotropic distribution. Planar structure extraction from point clouds is an important process in many applications, such as maintenance of infrastructure components including roads and curbstones, because most artificial structures consist of planar surfaces. A mobile mapping system (MMS) is able to obtain a large number of points while traveling at a standard speed. However, if a high-end laser scanning system is equipped, the point cloud has an anisotropic distribution. In traditional point-based methods, this causes problems when calculating geometric information using neighboring points. In the proposed method, the precise boundary of a planar structure is maintained by appropriately creating line segments from an input point cloud. Furthermore, a normal vector at a line segment is precisely estimated for the region growing process. An experiment using the point cloud from an MMS simulation indicates that the proposed method extracts planar regions accurately. Additionally, we apply the proposed method to several real point clouds and evaluate its effectiveness via visual inspection.

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