scholarly journals Effective Planar Cluster Detection in Point Clouds Using Histogram-Driven Kd-Like Partition and Shifted Mahalanobis Distance Based Regression

2019 ◽  
Vol 11 (21) ◽  
pp. 2465 ◽  
Author(s):  
Jakub Walczak ◽  
Tadeusz Poreda ◽  
Adam Wojciechowski
Keyword(s):  
Mahalanobis Distance ◽  
Point Cloud ◽  
State Of The Art ◽  
Point Clouds ◽  
Rejection Rate ◽  
Regular Space ◽  
Planar Surface ◽  
Point Cloud Segmentation ◽  
Partition Method ◽  
Excessive Number

Point cloud segmentation for planar surface detection is a valid problem of automatic laser scans analysis. It is widely exploited for many industrial remote sensing tasks, such as LIDAR city scanning, creating inventories of buildings, or object reconstruction. Many current methods rely on robustly calculated covariance and centroid for plane model estimation or global energy optimization. This is coupled with point cloud division strategies, based on uniform or regular space subdivision. These approaches result in many redundant divisions, plane maladjustments caused by outliers, and excessive number of processing iterations. In this paper, a new robust method of point clouds segmentation, based on histogram-driven hierarchical space division, inspired by kd-tree is presented. The proposed partition method produces results with a smaller oversegmentation rate. Moreover, state-of-the-art partitions often lead to nodes of low cardinality, which results in the rejection of many points. In the proposed method, the point rejection rate was reduced. Point cloud subdivision is followed by resilient plane estimation, using Mahalanobis distance with respect to seven cardinal points. These points were established based on eigenvectors of the covariance matrix of the considered point cluster. The proposed method shows high robustness and yields good quality metrics, much faster than a FAST-MCD approach. The overall results indicate improvements in terms of plane precision, plane recall, under-, and the over- segmentation rate with respect to the reference benchmark methods. Plane precision for the S3DIS dataset increased on average by 2.6pp and plane recall- by 3pp. Both over- and under- segmentation rates fell by 3.2pp and 4.3pp.

scholarly journals A Termination Criterion for Probabilistic Point Clouds Registration

Signals ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 159-173
Author(s):  
Simone Fontana ◽  
Domenico Giorgio Sorrenti
Keyword(s):  
State Of The Art ◽  
Point Clouds ◽  
Computational Time ◽  
Termination Criteria ◽  
Practical Usefulness ◽  
Local Point ◽  
Termination Criterion ◽  
Probabilistic Point ◽  
Excessive Number ◽  
Number Of Iterations

Probabilistic Point Clouds Registration (PPCR) is an algorithm that, in its multi-iteration version, outperformed state-of-the-art algorithms for local point clouds registration. However, its performances have been tested using a fixed high number of iterations. To be of practical usefulness, we think that the algorithm should decide by itself when to stop, on one hand to avoid an excessive number of iterations and waste computational time, on the other to avoid getting a sub-optimal registration. With this work, we compare different termination criteria on several datasets, and prove that the chosen one produces very good results that are comparable to those obtained using a very large number of iterations, while saving computational time.

scholarly journals Planar surface segmentation using a color-enhanced hybrid model for RGB-D camera-based indoor mobile mapping point clouds

2014 ◽  
Vol II-1 ◽  
pp. 61-67
Author(s):  
C. Wen ◽  
S. Lin ◽  
C. Wang ◽  
J. Li
Keyword(s):  
Point Cloud ◽  
Evaluation Process ◽  
Point Clouds ◽  
Planar Surface ◽  
Mobile Mapping ◽  
Hybrid Features ◽  
Segmentation Evaluation ◽  
Cloud Data ◽  
Surface Segmentation ◽  
Mapping Point

Point clouds acquired by RGB-D camera-based indoor mobile mapping system suffer the problems of being noisy, exhibiting an uneven distribution, and incompleteness, which are the problems that introduce difficulties for point cloud planar surface segmentation. This paper presents a novel color-enhanced hybrid planar surface segmentation model for RGB-D camera-based indoor mobile mapping point clouds based on region growing method, and the model specially addresses the planar surface extraction task over point cloud according to the noisy and incomplete indoor mobile mapping point clouds. The proposed model combines the color moments features with the curvature feature to select the seed points better. Additionally, a more robust growing criteria based on the hybrid features is developed to avoid the generation of excessive over-segmentation debris. A segmentation evaluation process with a small set of labeled segmented data is used to determine the optimal hybrid weight. Several comparative experiments were conducted to evaluate the segmentation model, and the experimental results demonstrate the effectiveness and efficiency of the proposed hybrid segmentation method for indoor mobile mapping three-dimensional (3D) point cloud data.

scholarly journals A comprehensive study of the rate-distortion performance in MPEG point cloud compression

2019 ◽  
Vol 8 ◽  
Author(s):  
Evangelos Alexiou ◽  
Irene Viola ◽  
Tomás M. Borges ◽  
Tiago A. Fonseca ◽  
Ricardo L. de Queiroz ◽  
...  
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Subjective Evaluation ◽  
Rate Distortion ◽  
Point Clouds ◽  
Current Approach ◽  
User Engagement ◽  
Viable Solution ◽  
Wide Range ◽  
Objective Quality

Abstract Recent trends in multimedia technologies indicate the need for richer imaging modalities to increase user engagement with the content. Among other alternatives, point clouds denote a viable solution that offers an immersive content representation, as witnessed by current activities in JPEG and MPEG standardization committees. As a result of such efforts, MPEG is at the final stages of drafting an emerging standard for point cloud compression, which we consider as the state-of-the-art. In this study, the entire set of encoders that have been developed in the MPEG committee are assessed through an extensive and rigorous analysis of quality. We initially focus on the assessment of encoding configurations that have been defined by experts in MPEG for their core experiments. Then, two additional experiments are designed and carried to address some of the identified limitations of current approach. As part of the study, state-of-the-art objective quality metrics are benchmarked to assess their capability to predict visual quality of point clouds under a wide range of radically different compression artifacts. To carry the subjective evaluation experiments, a web-based renderer is developed and described. The subjective and objective quality scores along with the rendering software are made publicly available, to facilitate and promote research on the field.

scholarly journals An Efficient Encoding Voxel-Based Segmentation (EVBS) Algorithm Based on Fast Adjacent Voxel Search for Point Cloud Plane Segmentation

2019 ◽  
Vol 11 (23) ◽  
pp. 2727 ◽  
Author(s):  
Ming Huang ◽  
Pengcheng Wei ◽  
Xianglei Liu
Keyword(s):  
Point Cloud ◽  
Initial Point ◽  
Computation Time ◽  
Region Growing ◽  
Point Clouds ◽  
Recall Rate ◽  
Seed Selection ◽  
Cloud Processing ◽  
Point Cloud Segmentation ◽  
Noise Data

Plane segmentation is a basic yet important process in light detection and ranging (LiDAR) point cloud processing. The traditional point cloud plane segmentation algorithm is typically affected by the number of point clouds and the noise data, which results in slow segmentation efficiency and poor segmentation effect. Hence, an efficient encoding voxel-based segmentation (EVBS) algorithm based on a fast adjacent voxel search is proposed in this study. First, a binary octree algorithm is proposed to construct the voxel as the segmentation object and code the voxel, which can compute voxel features quickly and accurately. Second, a voxel-based region growing algorithm is proposed to cluster the corresponding voxel to perform the initial point cloud segmentation, which can improve the rationality of seed selection. Finally, a refining point method is proposed to solve the problem of under-segmentation in unlabeled voxels by judging the relationship between the points and the segmented plane. Experimental results demonstrate that the proposed algorithm is better than the traditional algorithm in terms of computation time, extraction accuracy, and recall rate.

scholarly journals FAST PROBABILISTIC FUSION OF 3D POINT CLOUDS VIA OCCUPANCY GRIDS FOR SCENE CLASSIFICATION

2016 ◽  
Vol III-3 ◽  
pp. 325-332
Author(s):  
Andreas Kuhn ◽  
Hai Huang ◽  
Martin Drauschke ◽  
Helmut Mayer
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
State Of The Art ◽  
Point Clouds ◽  
Automatic Generation ◽  
Scene Classification ◽  
Semantic Classification ◽  
3D Point Clouds ◽  
Occupancy Grids ◽  
High Scalability

High resolution consumer cameras on Unmanned Aerial Vehicles (UAVs) allow for cheap acquisition of highly detailed images, e.g., of urban regions. Via image registration by means of Structure from Motion (SfM) and Multi View Stereo (MVS) the automatic generation of huge amounts of 3D points with a relative accuracy in the centimeter range is possible. Applications such as semantic classification have a need for accurate 3D point clouds, but do not benefit from an extremely high resolution/density. In this paper, we, therefore, propose a fast fusion of high resolution 3D point clouds based on occupancy grids. The result is used for semantic classification. In contrast to state-of-the-art classification methods, we accept a certain percentage of outliers, arguing that they can be considered in the classification process when a per point belief is determined in the fusion process. To this end, we employ an octree-based fusion which allows for the derivation of outlier probabilities. The probabilities give a belief for every 3D point, which is essential for the semantic classification to consider measurement noise. For an example point cloud with half a billion 3D points (cf. Figure 1), we show that our method can reduce runtime as well as improve classification accuracy and offers high scalability for large datasets.

scholarly journals A New Framework For Interactive Segmentation of Point Clouds

2014 ◽  
Vol XL-5 ◽  
pp. 357-362 ◽  
Author(s):  
K. Liu ◽  
J. Boehm
Keyword(s):  
Point Cloud ◽  
Fundamental Problem ◽  
Weighted Graph ◽  
Point Clouds ◽  
Target Object ◽  
General Point ◽  
Interactive Segmentation ◽  
Mobile Mapping ◽  
Point Cloud Segmentation ◽  
New Framework

Point cloud segmentation is a fundamental problem in point processing. Segmenting a point cloud fully automatically is very challenging due to the property of point cloud as well as different requirements of distinct users. In this paper, an interactive segmentation method for point clouds is proposed. Only two strokes need to be drawn intuitively to indicate the target object and the background respectively. The draw strokes are sparse and don't necessarily cover the whole object. Given the strokes, a weighted graph is built and the segmentation is formulated as a minimization problem. The problem is solved efficiently by using the Max Flow Min Cut algorithm. In the experiments, the mobile mapping data of a city area is utilized. The resulting segmentations demonstrate the efficiency of the method that can be potentially applied for general point clouds.

scholarly journals Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering

2020 ◽  
Vol 37 (6) ◽  
pp. 1019-1027
Author(s):  
Ali Saglam ◽  
Hasan B. Makineci ◽  
Ömer K. Baykan ◽  
Nurdan Akhan Baykan
Keyword(s):  
Point Cloud ◽  
Point Group ◽  
Three Dimensional ◽  
Point Clouds ◽  
Segmentation Method ◽  
Train Station ◽  
Cloud Processing ◽  
Point Cloud Processing ◽  
Point Cloud Segmentation ◽  
Geometric Cues

Point cloud processing is a struggled field because the points in the clouds are three-dimensional and irregular distributed signals. For this reason, the points in the point clouds are mostly sampled into regularly distributed voxels in the literature. Voxelization as a pretreatment significantly accelerates the process of segmenting surfaces. The geometric cues such as plane directions (normals) in the voxels are mostly used to segment the local surfaces. However, the sampling process may include a non-planar point group (patch), which is mostly on the edges and corners, in a voxel. These voxels can cause misleading the segmentation process. In this paper, we separate the non-planar patches into planar sub-patches using k-means clustering. The largest one among the planar sub-patches replaces the normal and barycenter properties of the voxel with those of itself. We have tested this process in a successful point cloud segmentation method and measure the effects of the proposed method on two point cloud segmentation datasets (Mosque and Train Station). The method increases the accuracy success of the Mosque dataset from 83.84% to 87.86% and that of the Train Station dataset from 85.36% to 87.07%.

scholarly journals SEGMENTATION OF LARGE UNSTRUCTURED POINT CLOUDS USING OCTREE-BASED REGION GROWING AND CONDITIONAL RANDOM FIELDS

2017 ◽  
Vol XLII-2/W8 ◽  
pp. 25-30 ◽  
Author(s):  
M. Bassier ◽  
M. Bonduel ◽  
B. Van Genechten ◽  
M. Vergauwen
Keyword(s):  
Random Fields ◽  
Point Cloud ◽  
Conditional Random Fields ◽  
Conditional Random Field ◽  
Region Growing ◽  
Point Clouds ◽  
Single Type ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Point Cloud Segmentation

Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it is a key aspect in the automated procedure from point cloud data to BIM. Current approaches typically only segment a single type of primitive such as planes or cylinders. Also, current algorithms suffer from oversegmenting the data and are often sensor or scene dependent.<br><br> In this work, a method is presented to automatically segment large unstructured point clouds of buildings. More specifically, the segmentation is formulated as a graph optimisation problem. First, the data is oversegmented with a greedy octree-based region growing method. The growing is conditioned on the segmentation of planes as well as smooth surfaces. Next, the candidate clusters are represented by a Conditional Random Field after which the most likely configuration of candidate clusters is computed given a set of local and contextual features. The experiments prove that the used method is a fast and reliable framework for unstructured point cloud segmentation. Processing speeds up to 40,000 points per second are recorded for the region growing. Additionally, the recall and precision of the graph clustering is approximately 80%. Overall, nearly 22% of oversegmentation is reduced by clustering the data. These clusters will be classified and used as a basis for the reconstruction of BIM models.

scholarly journals Pointwise Rotation-Invariant Network with Adaptive Sampling and 3D Spherical Voxel Convolution

2020 ◽  
Vol 34 (07) ◽  
pp. 12717-12724
Author(s):  
Yang You ◽  
Yujing Lou ◽  
Qi Liu ◽  
Yu-Wing Tai ◽  
Lizhuang Ma ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Point Cloud ◽  
Data Augmentation ◽  
Feature Matching ◽  
State Of The Art ◽  
Point Clouds ◽  
Rotation Invariant ◽  
Learning Framework ◽  
Point Set ◽  
Part Segmentation

Point cloud analysis without pose priors is very challenging in real applications, as the orientations of point clouds are often unknown. In this paper, we propose a brand new point-set learning framework PRIN, namely, Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature extraction in point clouds analysis. We construct spherical signals by Density Aware Adaptive Sampling to deal with distorted point distributions in spherical space. In addition, we propose Spherical Voxel Convolution and Point Re-sampling to extract rotation-invariant features for each point. Our network can be applied to tasks ranging from object classification, part segmentation, to 3D feature matching and label alignment. We show that, on the dataset with randomly rotated point clouds, PRIN demonstrates better performance than state-of-the-art methods without any data augmentation. We also provide theoretical analysis for the rotation-invariance achieved by our methods.

scholarly journals MLS POINT CLOUD SEGMENTATION BASED ON FEATURE POINTS OF SCANLINES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1007-1013
Author(s):  
R. Honma ◽  
H. Date ◽  
S. Kanai
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Spatial Distance ◽  
Feature Points ◽  
Segmentation Method ◽  
Point Reduction ◽  
Point Cloud Segmentation ◽  
Edge Points ◽  
The Difference

<p><strong>Abstract.</strong> Point clouds acquired using Mobile Laser Scanning (MLS) are applied to extract road information such as curb stones, road markings, and road side objects. In this paper, we present a scanline-based MLS point cloud segmentation method for various road and road side objects. First, end points of the scanline, jump edge points, and corner points are extracted as feature points. The feature points are then interpolated to accurately extract irregular parts consisting of irregularly distributed points such as vegetation. Next, using a point reduction method, additional feature points on a smooth surface are extracted for segmentation at the edges of the curb cut. Finally, points between the feature points are extracted as flat segments on the scanline, and continuing feature points are extracted as irregular segments on the scanline. Furthermore, these segments on the scanline are integrated as flat or irregular regions. In the extraction of the feature points, neighboring points based on the spatial distance are used to avoid being influenced by the difference in the point density. Based on experiments, the effectiveness of the proposed method was indicated based on an application to an MLS point cloud.</p>

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