Development of tomato segmentation algorithm using normal vectors of 3D point clouds

Normal vector of 3D surface is important differential geometric property over localized neighborhood, and its abrupt change along the surface directly reflects the variation of geometric morphometric. Based on this observation, this paper presents a novel edge detection algorithm in 3D point clouds, which utilizes the change intensity and change direction of adjacent normal vectors and is composed of three steps. First, a two-dimensional grid is constructed according to the inherent data acquisition sequence so as to build up the topology of points. Second, by this topological structure preliminary edge points are retrieved, and the potential directions of edges passing through them are estimated according to the change of normal vectors between adjacent points. Finally, an edge growth strategy is designed to regain the missing edge points and connect them into complete edge lines. The results of experiment in a real scene demonstrate that the proposed algorithm can extract geometric edges from 3D point clouds robustly, and is able to reduce edge quality’s dependence on user defined parameters.

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AN IMPROVED COHERENT POINT DRIFT METHOD FOR TLS POINT CLOUD REGISTRATION OF COMPLEX SCENES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1169-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1169-1175

Author(s):

Y. Zang ◽

R. C. Lindenbergh

Keyword(s):

Likelihood Function ◽

Point Clouds ◽

Automatic Registration ◽

Covariance Descriptor ◽

Uniform Sampling ◽

3D Point Clouds ◽

Complex Scenes ◽

Subsequent Step ◽

Coherent Point Drift ◽

Normal Vectors

Abstract. Processing unorganized 3D point clouds is highly desirable, especially for the applications in complex scenes (such as: mountainous or vegetation areas). Registration is the precondition to obtain complete surface information of complex scenes. However, for complex environment, the automatic registration of TLS point clouds is still a challenging problem. In this research, we propose an automatic registration for TLS point clouds of complex scenes based on coherent point drift (CPD) algorithm combined with a robust covariance descriptor. Out method consists of three steps: the construction of the covariance descriptor, uniform sampling of point clouds, and CPD optimization procedures based on Expectation-Maximization (EM algorithm). In the first step, we calculate a feature vector to construct a covariance matrix for each point based on the estimated normal vectors. In the subsequent step, to ensure efficiency, we use uniform sampling to obtain a small point set from the original TLS data. Finally, we form an objective function combining the geometric information described by the proposed descriptor, and optimize the transformation iteratively by maximizing the likelihood function. The experimental results on the TLS datasets of various scenes demonstrate the reliability and efficiency of the proposed method. Especially for complex environments with disordered vegetation or point density variations, this method can be much more efficient than original CPD algorithm.

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ACTIVE SAMPLING AND MODEL BASED PREDICTION FOR FAST AND ROBUST DETECTION AND RECONSTRUCTION OF COMPLEX ROOFS IN 3D POINT CLOUDS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w8-43-2019 ◽

2019 ◽

Vol IV-4/W8 ◽

pp. 43-50

Author(s):

Y. Dehbi ◽

A. Henn ◽

G. Gröger ◽

V. Stroh ◽

L. Plümer

Keyword(s):

Energy Demand ◽

Early Stage ◽

Point Clouds ◽

Support Vector ◽

Active Sampling ◽

Hybrid Strategy ◽

Robust Detection ◽

Density Distributions ◽

3D Point Clouds ◽

Normal Vectors

Abstract. 3D city models in Level-of-Detail 2 (LoD2) are nowadays inevitable for many applications such as solar radiation calculation and energy demand estimation. City-wide models are required which can solely be acquired by fully automatic approaches. In this paper we propose a novel method for the 3D-reconstruction of LoD2 buildings with structured roofs and dormers from LIDAR data. We apply a hybrid strategy which combines the strengths of top-down and bottom-up methods. The main contribution is the introduction of an active sampling strategy which applies a cascade of filters focusing on promising samples in an early stage and avoiding the pitfalls of RANSAC based approaches. Such filters are based on prior knowledge represented by (non-parametric) density distributions. Samples are pairs of surflets, i.e. 3D points together with normal vectors derived from a plane approximation of their neighborhood. Surflet pairs imply immediately important roof parameters such as azimuth, inclination and ridge height, as well as parameters for internal precision and consistency, giving a good base for assessment and ranking. Ranking of samples leads to a small number of promising hypotheses. Model selection is based on predictions for example of ridge positions which can easily be falsified based on the given observations. Our approach does not require building footprints as prerequisite. They are derived in a preprocessing step using machine learning methods, in particular Support Vector Machines (SVM).

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FEATURE RELEVANCE ANALYSIS FOR 3D POINT CLOUD CLASSIFICATION USING DEEP LEARNING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w5-373-2019 ◽

2019 ◽

Vol IV-2/W5 ◽

pp. 373-380 ◽

Cited By ~ 1

Author(s):

A. Kumar ◽

K. Anders ◽

L Winiwarter ◽

B. Höfle

Keyword(s):

Deep Learning ◽

Point Cloud ◽

Laser Scanning ◽

Point Clouds ◽

Normal Vector ◽

3D Point Cloud ◽

Point Distribution ◽

Spatial Point ◽

3D Point Clouds ◽

Normal Vectors

Abstract. 3D point clouds acquired by laser scanning and other techniques are difficult to interpret because of their irregular structure. To make sense of this data and to allow for the derivation of useful information, a segmentation of the points in groups, units, or classes fit for the specific use case is required. In this paper, we present a non-end-to-end deep learning classifier for 3D point clouds using multiple sets of input features and compare it with an implementation of the state-of-the-art deep learning framework PointNet++. We first start by extracting features derived from the local normal vector (normal vectors, eigenvalues, and eigenvectors) from the point cloud, and study the result of classification for different local search radii. We extract additional features related to spatial point distribution and use them together with the normal vector-based features. We find that the classification accuracy improves by up to 33% as we include normal vector features with multiple search radii and features related to spatial point distribution. Our method achieves a mean Intersection over Union (mIoU) of 94% outperforming PointNet++’s Multi Scale Grouping by up to 12%. The study presents the importance of multiple search radii for different point cloud features for classification in an urban 3D point cloud scene acquired by terrestrial laser scanning.

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PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-3-201-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 201-208 ◽

Cited By ~ 1

Author(s):

Xiaohu Lu ◽

Jian Yao ◽

Jinge Tu ◽

Kai Li ◽

Li Li ◽

...

Keyword(s):

Point Cloud ◽

Clustering Algorithm ◽

Input Parameter ◽

Point Clouds ◽

Segmentation Algorithm ◽

Experimental Results ◽

3D Point Cloud ◽

3D Point Clouds ◽

Point Cloud Segmentation ◽

Data Point

In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.

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PAIRWISE LINKAGE FOR POINT CLOUD SEGMENTATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-3-201-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 201-208 ◽

Cited By ~ 13

Author(s):

Xiaohu Lu ◽

Jian Yao ◽

Jinge Tu ◽

Kai Li ◽

Li Li ◽

...

Keyword(s):

Point Cloud ◽

Clustering Algorithm ◽

Input Parameter ◽

Point Clouds ◽

Segmentation Algorithm ◽

Experimental Results ◽

3D Point Cloud ◽

3D Point Clouds ◽

Point Cloud Segmentation ◽

Data Point

In this paper, we first present a novel hierarchical clustering algorithm named Pairwise Linkage (P-Linkage), which can be used for clustering any dimensional data, and then effectively apply it on 3D unstructured point cloud segmentation. The P-Linkage clustering algorithm first calculates a feature value for each data point, for example, the density for 2D data points and the flatness for 3D point clouds. Then for each data point a pairwise linkage is created between itself and its closest neighboring point with a greater feature value than its own. The initial clusters can further be discovered by searching along the linkages in a simple way. After that, a cluster merging procedure is applied to obtain the finally refined clustering result, which can be designed for specialized applications. Based on the P-Linkage clustering, we develop an efficient segmentation algorithm for 3D unstructured point clouds, in which the flatness of the estimated surface of a 3D point is used as its feature value. For each initial cluster a slice is created, then a novel and robust slicemerging method is proposed to get the final segmentation result. The proposed P-Linkage clustering and 3D point cloud segmentation algorithms require only one input parameter in advance. Experimental results on different dimensional synthetic data from 2D to 4D sufficiently demonstrate the efficiency and robustness of the proposed P-Linkage clustering algorithm and a large amount of experimental results on the Vehicle-Mounted, Aerial and Stationary Laser Scanner point clouds illustrate the robustness and efficiency of our proposed 3D point cloud segmentation algorithm.

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Multi-scale Information Extraction Residual Network for 3D Point Clouds Classification

2020 Chinese Automation Congress (CAC) ◽

10.1109/cac51589.2020.9327126 ◽

2020 ◽

Author(s):

Yumei Li ◽

Shan Meng ◽

Daoyuan Liang

Keyword(s):

Information Extraction ◽

Point Clouds ◽

Residual Network ◽

Multi Scale ◽

3D Point Clouds

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Symmetry Analysis of Oriental Polygonal Pagodas Using 3D Point Clouds for Cultural Heritage

Sensors ◽

10.3390/s21041228 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1228

Author(s):

Ting On Chan ◽

Linyuan Xia ◽

Yimin Chen ◽

Wei Lang ◽

Tingting Chen ◽

...

Keyword(s):

Cultural Heritage ◽

Unmanned Aerial Vehicle ◽

Point Cloud ◽

Geometric Model ◽

Digital Camera ◽

Point Clouds ◽

Symmetry Analysis ◽

3D Point Clouds ◽

Transmission Towers ◽

Aerial Vehicle

Ancient pagodas are usually parts of hot tourist spots in many oriental countries due to their unique historical backgrounds. They are usually polygonal structures comprised by multiple floors, which are separated by eaves. In this paper, we propose a new method to investigate both the rotational and reflectional symmetry of such polygonal pagodas through developing novel geometric models to fit to the 3D point clouds obtained from photogrammetric reconstruction. The geometric model consists of multiple polygonal pyramid/prism models but has a common central axis. The method was verified by four datasets collected by an unmanned aerial vehicle (UAV) and a hand-held digital camera. The results indicate that the models fit accurately to the pagodas’ point clouds. The symmetry was realized by rotating and reflecting the pagodas’ point clouds after a complete leveling of the point cloud was achieved using the estimated central axes. The results show that there are RMSEs of 5.04 cm and 5.20 cm deviated from the perfect (theoretical) rotational and reflectional symmetries, respectively. This concludes that the examined pagodas are highly symmetric, both rotationally and reflectionally. The concept presented in the paper not only work for polygonal pagodas, but it can also be readily transformed and implemented for other applications for other pagoda-like objects such as transmission towers.

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Using Least-Squares Residuals to Assess the Stochasticity of Measurements—Example: Terrestrial Laser Scanner and Surface Modeling

Engineering Proceedings ◽

10.3390/engproc2021005059 ◽

2021 ◽

Vol 5 (1) ◽

pp. 59

Author(s):

Gaël Kermarrec ◽

Niklas Schild ◽

Jan Hartmann

Keyword(s):

Least Squares ◽

Laser Scanner ◽

Real Data ◽

Point Clouds ◽

Statistical Testing ◽

Normal Vector ◽

Engineering Structures ◽

3D Point Clouds ◽

Least Squares Adjustment ◽

Correlation Parameters

Terrestrial laser scanners (TLS) capture a large number of 3D points rapidly, with high precision and spatial resolution. These scanners are used for applications as diverse as modeling architectural or engineering structures, but also high-resolution mapping of terrain. The noise of the observations cannot be assumed to be strictly corresponding to white noise: besides being heteroscedastic, correlations between observations are likely to appear due to the high scanning rate. Unfortunately, if the variance can sometimes be modeled based on physical or empirical considerations, the latter are more often neglected. Trustworthy knowledge is, however, mandatory to avoid the overestimation of the precision of the point cloud and, potentially, the non-detection of deformation between scans recorded at different epochs using statistical testing strategies. The TLS point clouds can be approximated with parametric surfaces, such as planes, using the Gauss–Helmert model, or the newly introduced T-splines surfaces. In both cases, the goal is to minimize the squared distance between the observations and the approximated surfaces in order to estimate parameters, such as normal vector or control points. In this contribution, we will show how the residuals of the surface approximation can be used to derive the correlation structure of the noise of the observations. We will estimate the correlation parameters using the Whittle maximum likelihood and use comparable simulations and real data to validate our methodology. Using the least-squares adjustment as a “filter of the geometry” paves the way for the determination of a correlation model for many sensors recording 3D point clouds.

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