基于机载激光雷达点云的断裂线自动提取方法

基于机载激光雷达（LIDAR）点云生产高精度的数字高程模型（DTM）需要进行断裂线的存储与表达,在分析现有断裂线提取方法不足的基础上,提出一种从LIDAR点云自动提取断裂线的方法。该方法利用离散的点云构建三角网,建立点云之间的拓扑关系,根据三角网面片之间的法向差异提取候选断裂线点,采用“方向优先”追踪策略实现断裂线的追踪处理,并利用“线性迭代法”实现断裂线的光滑输出。实验结果表明,该方法可以快速从LIDAR点云中自动提取断裂线信息,具有一定的应用价值。     

Roof plane extraction from airborne lidar point clouds

Planar patches are important primitives for polyhedral building models. One of the key challenges for successful reconstruction of three-dimensional (3D) building models from airborne lidar point clouds is achieving high quality recognition and segmentation of the roof planar points. Unfortunately, the current automatic extraction processes for planar surfaces continue to suffer from limitations such as sensitivity to the selection of seed points and the lack of computational efficiency. In order to address these drawbacks, a new fully automatic segmentation method is proposed in this article, which is capable of the following: (1) processing a roof point dataset with an arbitrary shape; (2) robustly selecting the seed points in a parameter space with reduced dimensions; and (3) segmenting the planar patches in a sub-dataset with similar attributes when region growing in the object space. The detection of seed points in the parameter space was improved by mapping the accumulator array to a 1D space. The range for region growing in the object space was reduced by an attribute similarity measure that split the roof dataset into candidate and non-candidate subsets. The experimental results confirmed that the proposed approach can extract planar patches of building roofs robustly and efficiently.     

Automated extraction of street-scene objects from mobile lidar point clouds

Mobile laser scanning or lidar is a new and rapid system to capture high-density three-dimensional (3-D) point clouds. Automatic data segmentation and feature extraction are the key steps for accurate identification and 3-D reconstruction of street-scene objects (e.g. buildings and trees). This article presents a novel method for automated extraction of street-scene objects from mobile lidar point clouds. The proposed method first uses planar division to sort points into different grids, then calculates the weights of points in each grid according to the spatial distribution of mobile lidar points and generates the geo-referenced feature image of the point clouds using the inverse-distance-weighted interpolation method. Finally, the proposed method transforms the extraction of street-scene objects from 3-D mobile lidar point clouds into the extraction of geometric features from two-dimensional (2-D) imagery space, thus simplifying the automated object extraction process. Experimental results show that the proposed method provides a promising solution for automatically extracting street-scene objects from mobile lidar point clouds.     

Multi-scale tensor voting for feature extraction from unstructured point clouds

Identifying sharp features in a 3D model is essential for shape analysis, matching and a wide range of geometry processing applications. This paper presents a new method based on the tensor voting theory to extract sharp features from an unstructured point cloud which may contain random noise, outliers and artifacts. Our method first takes the voting tensors at every point using the corresponding neighborhoods and computes the feature weight to infer the local structure via eigenvalue analysis of the tensor. The optimal scale for a point is automatically determined by observing the feature weight variation in order to deal with both a noisy smooth region and a sharp edge. We finally extract the points at sharp features using adaptive thresholding of the feature weight and the feature completion process. The multi-scale tensor voting of a given point set improves noise sensitivity and scale dependency of an input model. We demonstrate the strength of the proposed method in terms of efficiency and robustness by comparing it with other feature detection algorithms.     

Feature line extraction from unorganized noisy point clouds using truncated Fourier series

The detection of feature lines is important for representing and understanding geometric features of 3D models. In this paper, we introduce a new and robust method for extracting feature lines from unorganized point clouds. We use a one-dimensional truncated Fourier series for detecting feature points. Each point and its neighbors are approximated along the principal directions by using the truncated Fourier series, and the curvature of the point is computed from the approximated curves. The Fourier coefficients are computed by Fast Fourier Transform (FFT). We apply low-pass filtering to remove noise and to compute the curvature of the point robustly. For extracting feature points from the detected potential feature points, the potential feature points are thinned using a curvature weighted Laplacian-like smoothing method. The feature lines are constructed through growing extracted points and then projected onto the original point cloud. The efficiency and robustness of our approach is illustrated by several experimental results.     

An evolutionary approach to the extraction of object construction trees from 3D point clouds

In order to extract a construction tree from a finite set of points sampled on the surface of an object, we present an evolutionary algorithm that evolves set-theoretic expressions made of primitives fitted to the input point-set and modeling operations. To keep relatively simple trees, we use a penalty term in the objective function optimized by the evolutionary algorithm. We show with experiments successes but also limitations of this approach.     

3D object recognition method with multiple feature extraction from LiDAR point clouds

During autonomous driving, fast and accurate object recognition supports environment perception for local path planning of unmanned ground vehicles. Feature extraction and object recognition from large-scale 3D point clouds incur massive computational and time costs. To implement fast environment perception, this paper proposes a 3D recognition system with multiple feature extraction from light detection and ranging point clouds modified by parallel computing. Effective object feature extraction is a necessary step prior to executing an object recognition procedure. In the proposed system, multiple geometry features of a point cloud that resides in corresponding voxels are computed concurrently. In addition, a scale filter is employed to convert feature vectors from uncertain count voxels to a normalized object feature matrix, which is convenient for object-recognizing classifiers. After generating the object feature matrices of all voxels, an initialized multilayer neural network (NN) model is trained offline through a large number of iterations. Using the trained NN model, real-time object recognition is realized using parallel computing technology to accelerate computation.

     

一种非封闭自由曲面的点云边界提取算法

点云边界不仅作为表达曲面的重要的几何特征,而且作为求解曲面的定义域,对重建曲面模型的品质和精度起着重要的作用.以激光线性均匀扫描的点云数据为例论述了一种改进的空间非封闭自由曲面点云的边界提取方法,在原算法基础上增设阈值,变固定K值为变量K值.实验证明该算法不仅可以较快地提取边界,而且表达曲面边界特征比较精确.     

An algorithm for the rapid generation of bas-reliefs based on point clouds

This paper proposes an algorithm for the rapid generation of bas-reliefs based on point clouds, which involves two steps: generation of a coarse model and establishment of fine mesh surfaces of the model. In the first step, a modified Z-Buffer algorithm is adopted to designate the visibility of every point before control points in a gridded distribution are arranged on the base plane of a relief. Afterwards, under the constraints of depth and normal information, the optimal compression ratio of the control points is obtained through use of a linear solution. For the compression ratio, bilinear interpolation is performed to generate an original model of the relief. In the establishment of the fine mesh surfaces, an index for measuring surface changes is proposed to adjust the height of the relief once again so as to highlight its detailed features. The aforementioned algorithm is verified by experimental work.     

基于特征提取的点云自动配准优化研究

针对三维点云自动配准精度不高、鲁棒性不强等问题,提出一种基于判断点云邻域法向量夹角的自动配准算法。该算法首先计算点云中每个点的法向量与邻域点集的法向量夹角的余弦值,然后把邻域各点的余弦值作为该点的属性特征向量,进行特征分类提取特征点,根据几何特征的相似性初步搜索匹配点对,并采用欧式距离约束条件剔除匹配错误的点对;运用最小二乘法计算初始配准参数,再通过改进的迭代最近点（Iterative Closest Point,ICP）算法进行精匹配。实验证明,该算法相对于经典的ICP算法无论收敛速度还是匹配精度上都有提升。     

Shape classification guided method for automated extraction of urban trees from terrestrial laser scanning point clouds

Accurate detection and extraction of individual trees is one of hottest topics, which can be widely used in vehicles navigation, tree modeling, tree growth monitoring and urban green quantity estimation. The difficulty associated with individual trees extraction is the occlusion with other objects in cluttered point clouds of urban scenes, which inhibits the automatic extraction of individual trees. In this paper, we present a comprehensive framework that can be used to extract individual trees from terrestrial scanned outdoor scene. In our framework, a bottom-up method by shape-guided classification is achieved to select the candidate tree crowns and tree trunks, and a novel three-stage shape merging rule containing localization, filtering, and matching (LFM) are proposed to generate a complete individual tree. The primary advantage of the proposed method is that it is independent of the quality of data and different shapes. We made comparison experiments of classification methods of support vector machine and random forest on the accuracy assessment. The effectiveness of the proposed framework was tested in five street scenarios in point clouds from Oakland outdoor MLS dataset. The results for the five test sites achieved tree detection rates higher than 97%; the overall accuracy was approximately 98%, and the completion quality of both procedures was 96%. Non-detected trees are always sparse which come from occlusions in the point cloud data; most misclassifications occurred in man-made pillars adjacent to trees and have the same height with tree trunk. Comparison experiments to the existing methods are made to illustrate the effectiveness of our method.

     

Reconstruction of piecewise planar objects from point clouds

This article discusses the reverse engineering problem of reconstructing objects with planar faces. We will present the main geometric features of a modeling system which are the detection of planar faces and the generation of a cad model. The algorithms are applied to the problem of reconstruction of buildings from airborne laser scanner data.     

Extracting lines of curvature from noisy point clouds

We present a robust framework for extracting lines of curvature from point clouds. First, we show a novel approach to denoising the input point cloud using robust statistical estimates of surface normal and curvature which automatically rejects outliers and corrects points by energy minimization. Then the lines of curvature are constructed on the point cloud with controllable density. Our approach is applicable to surfaces of arbitrary genus, with or without boundaries, and is statistically robust to noise and outliers while preserving sharp surface features. We show our approach to be effective over a range of synthetic and real-world input datasets with varying amounts of noise and outliers. The extraction of curvature information can benefit many applications in CAD, computer vision and graphics for point cloud shape analysis, recognition and segmentation. Here, we show the possibility of using the lines of curvature for feature-preserving mesh construction directly from noisy point clouds.     

Adapted Delaunay triangulation method for free-form surface generation from random point clouds for stochastic optimization applications

Free-form surfaces are defined with NURBS (non-uniform rational basis spline) for most computer-aided engineering (CAE) applications. The NURBS method requ     

基于机载激光雷达点云的交互式树木分割与建模方法研究

机载激光雷达扫描(ALS)系统可大规模获取地表树木点云,有助于较高精度树木结构参数提取和景观层面的几何重建,然而树木复杂的拓扑结构和树种的多样性给树木的准确分割与建模带来挑战.传统基于点云的自动树木分割和建模算法虽然效率高,但存在分割误差较大、建模鲁棒性较差等问题,难以满足深度学习大背景下用户对树木分割与建模结果进行精...     

Building a water feature extraction model by integrating aerial image and lidar point clouds

An innovative model for extracting water regions from aerial images fused with light detection and ranging (lidar) data is proposed in this article. This model extracts water features from coarse to fine levels of accuracy by considering special spectral bands of existing airborne lidar systems and their spectral characteristics. The particular model consists of two parts, namely inexact water region recognition and precise water extraction. (1) A strategy of using a triangulated irregular network (TIN) is introduced to describe point clouds with a particular structure. A TIN coarsely divides the network into water and non-water regions through a threshold, which can be determined through an equation by inputting the minimum width and point density of water regions. The coarsely defined water region can be detected through overlay analysis between the aerial image and the raster surface generated from the TIN. (2) An improved mean-shift algorithm is used to remove most land pixels from the roughly recognized water to obtain precise water edges from coarse water. A new empirical formula to describe distance between multi-dimensional data is adopted. Using the mean-shift algorithm and empirical distance function, accurate water edge features are extracted from inexact water region(s). In addition, the classification field of lidar point clouds is used to remove land pixels from water features.

A case study based on a point cloud data set and an aerial image is conducted to evaluate the feasibility and accuracy of the proposed model. Spatial distances between checkpoints and extracted water edges, as well as the confusion matrix of mean-shift classification, are adopted as measurements of accuracy for the extracted water edges in two case regions. Evaluation results show that the proposed model achieved continuous water-edge features, and that spatial accuracy of water edges is 0.3 to 0.4 m, at approximately the 1–2-pixel level, which is more than four times better than the maximum-likelihood classification method. General accuracy of the confusion matrix shows that mean-shift classification in the proposed model is better than 95%, which indicates excellent results.     

Classification of image matching point clouds over an urban area

Airborne laser scanning (ALS) and image matching are the two main techniques for generating point clouds for large areas. While the classification of ALS point clouds has been well investigated, there are few studies that are related to image matching point clouds. In this study, point clouds of multiple resolutions from high-resolution aerial images (ground sampling distance, GSD, of 6 cm) over the city of Vienna were generated and investigated with respect to point density and processing time. Three different study sites with various urban structures are selected from a bigger dataset and classified based on two different approaches: machine learning and a traditional operator-based decision tree. Classification accuracy was evaluated and compared with confusion matrices. In general, the machine learning method results in a higher overall accuracy compared to the simple decision tree method, with accuracies of 87% and 84%, respectively, at the highest resolution. At lower-resolution levels (GSDs of 12 cm and 24 cm), the overall accuracy of machine learning drops by 4% and that of the simple decision tree by 7% for each level. Classifying rasterized data instead of the original point cloud resulted in an accuracy drop of 5%. Thus, using machine learning on point clouds at the highest available resolution is suggested for classification of urban areas.