Research on geometric features and point cloud properties for tree skeleton extraction

Abstract. This work presents an extended photogrammetric pipeline aimed to improve 3D reconstruction results. Standard photogrammetric pipelines can produce noisy 3D data, especially when images are acquired with various sensors featuring different properties. In this paper, we propose an automatic filtering procedure based on some geometric features computed on the sparse point cloud created within the bundle adjustment phase. Bad 3D tie points and outliers are detected and removed, relying on micro and macro-clusters analyses. Clusters are built according to the prevalent dimensionality class (1D, 2D, 3D) assigned to low-entropy points, and corresponding to the main linear, planar o scatter local behaviour of the point cloud. While the macro-clusters analysis removes smallsized clusters and high-entropy points, in the micro-clusters investigation covariance features are used to verify the inner coherence of each point to the assigned class. Results on heritage scenarios are presented and discussed.

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Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features

Remote Sensing ◽

10.3390/rs11161955 ◽

2019 ◽

Vol 11 (16) ◽

pp. 1955 ◽

Cited By ~ 4

Author(s):

Markus Hillemann ◽

Martin Weinmann ◽

Markus S. Mueller ◽

Boris Jutzi

Keyword(s):

Spatial Data ◽

Point Cloud ◽

Laser Scanner ◽

The Self ◽

Geometric Features ◽

Mobile Mapping ◽

Extrinsic Calibration ◽

Self Calibration ◽

Mobile Mapping Systems ◽

Mapping Systems

Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated.

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Force field driven skeleton extraction method for point cloud trees

Earth Science Informatics ◽

10.1007/s12145-018-0365-3 ◽

2018 ◽

Vol 12 (2) ◽

pp. 161-171

Author(s):

Linming Gao ◽

Dong Zhang ◽

Nan Li ◽

Lei Chen

Keyword(s):

Force Field ◽

Extraction Method ◽

Point Cloud ◽

Skeleton Extraction

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Separating Leaf and Wood Points in Terrestrial Laser Scanning Data Using Multiple Optimal Scales

Sensors ◽

10.3390/s19081852 ◽

2019 ◽

Vol 19 (8) ◽

pp. 1852 ◽

Cited By ~ 1

Author(s):

Junjie Zhou ◽

Hongqiang Wei ◽

Guiyun Zhou ◽

Lihui Song

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Terrestrial Laser Scanning ◽

Geometric Features ◽

Optimization Strategy ◽

Running Time ◽

Multi Scale ◽

Scale Method ◽

Optimal Scale

The separation of leaf and wood points is an essential preprocessing step for extracting many of the parameters of a tree from terrestrial laser scanning data. The multi-scale method and the optimal scale method are two of the most widely used separation methods. In this study, we extend the optimal scale method to the multi-optimal-scale method, adaptively selecting multiple optimal scales for each point in the tree point cloud to increase the distinctiveness of extracted geometric features. Compared with the optimal scale method, our method achieves higher separation accuracy. Compared with the multi-scale method, our method achieves more stable separation accuracy with a limited number of optimal scales. The running time of our method is greatly reduced when the optimization strategy is applied.

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Underwater Matching Correction Navigation Based on Geometric Features Using Sonar Point Cloud Data

Scientific Programming ◽

10.1155/2017/7136702 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 4

Author(s):

Mingjie Dong ◽

Wusheng Chou ◽

Bin Fang

Keyword(s):

Power Plant ◽

Nuclear Power Plant ◽

Point Cloud ◽

Nuclear Power ◽

A Priori ◽

Geometric Features ◽

Water Tank ◽

Point Cloud Data ◽

Cloud Data ◽

Reactor Pool

In order to localize the Remotely Operated Vehicle (ROV) accurately in the reactor pool of the nuclear power plant, an underwater matching correction navigation algorithm based on geometric features using sonar point cloud data is proposed. At first, an Extended Kalman Filter (EKF) is used to compensate the motion induced distortion after the preprocessing of the sonar point cloud data. Then, the adjacent scanning point cloud data are fitted to be four different straight lines using Hough Transform and least square method. After that, the adjacent straight line is modified based on geometric features to get a standard rectangle. Since the working environment of the ROV is a rectangular shape with all dimensions known, it is used as a priori map. The matching rectangle is then used to compare with the a priori map to calculate the accurate position and orientation of the ROV. The obtained result is then applied as the measurement for the second EKF to obtain better localization accuracy. Experiments have been conducted in man-made water tank which is similar to the reactor pool of the nuclear power plant, and the results successfully verify the effectiveness of the proposed algorithm.

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Feature Extraction from 3D Point Cloud Data Based on Discrete Curves

Mathematical Problems in Engineering ◽

10.1155/2013/290740 ◽

2013 ◽

Vol 2013 ◽

pp. 1-19 ◽

Cited By ~ 6

Author(s):

Yi An ◽

Zhuohan Li ◽

Cheng Shao

Keyword(s):

Feature Extraction ◽

Autonomous Navigation ◽

Point Cloud ◽

Geometric Features ◽

3D Point Cloud ◽

Point Cloud Data ◽

Principal Curvatures ◽

Cloud Data ◽

Discrete Curves ◽

Threshold Setting

Reliable feature extraction from 3D point cloud data is an important problem in many application domains, such as reverse engineering, object recognition, industrial inspection, and autonomous navigation. In this paper, a novel method is proposed for extracting the geometric features from 3D point cloud data based on discrete curves. We extract the discrete curves from 3D point cloud data and research the behaviors of chord lengths, angle variations, and principal curvatures at the geometric features in the discrete curves. Then, the corresponding similarity indicators are defined. Based on the similarity indicators, the geometric features can be extracted from the discrete curves, which are also the geometric features of 3D point cloud data. The threshold values of the similarity indicators are taken from[0,1], which characterize the relative relationship and make the threshold setting easier and more reasonable. The experimental results demonstrate that the proposed method is efficient and reliable.

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Curve skeleton extraction from incomplete point cloud

ACM Transactions on Graphics ◽

10.1145/1531326.1531377 ◽

2009 ◽

Vol 28 (3) ◽

pp. 1-9 ◽

Cited By ~ 113

Author(s):

Andrea Tagliasacchi ◽

Hao Zhang ◽

Daniel Cohen-Or

Keyword(s):

Point Cloud ◽

Skeleton Extraction ◽

Curve Skeleton

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GEOMETRIC FEATURES AND THEIR RELEVANCE FOR 3D POINT CLOUD CLASSIFICATION

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-w1-157-2017 ◽

2017 ◽

Vol IV-1/W1 ◽

pp. 157-164 ◽

Cited By ~ 7

Author(s):

M. Weinmann ◽

B. Jutzi ◽

C. Mallet ◽

M. Weinmann

Keyword(s):

Influencing Factors ◽

Point Cloud ◽

Geometric Features ◽

3D Point Cloud ◽

Point Sampling ◽

Cloud Data ◽

Semantic Classes ◽

Automatic Interpretation ◽

Point Cloud Classification ◽

The Impact

In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (linear structures, planar structures and volumetric structures) as well as a reference labeling with respect to five semantic classes (Wire, Pole/Trunk, Façade, Ground and Vegetation) is available.

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