Unorganized Point Clouds Research Articles

Surface reconstruction from unorganized point clouds based on edge growing

Owing to unorganized point cloud data, unexpected triangles, such as holes and slits, may be generated during mesh surface reconstruction. To solve this problem, a mesh surface reconstruction method based on edge growing from unorganized point clouds is proposed. The method first constructs an octree structure for unorganized point cloud data, and determines the k-nearest neighbor for each point. Subsequently, the method searches for flat areas in the point clouds to be used as the initial mesh edge growth regions, to avoid incorrect reconstruction of the mesh surface owing to the growth of initial sharp areas. Finally, the optimal mesh surface is obtained by controlling the mesh edge growing based on compulsive restriction and comprehensive optimization criteria. The experimental results of mesh surface reconstruction show that the method is feasible and shows high reconstruction performance without introducing holes or slits in the reconstructed mesh surface.

Semiautomatic Construction of 2-D Façade Footprints From Mobile LiDAR Data

Although mobile light detection and ranging (LiDAR) technology has excellent potential in mapping street scenes, there is little research in constructing facade footprints from unorganized, uneven, and incomplete mobile LiDAR point clouds. In fact, facade footprint vectorization from mobile LiDAR data still involves a lot of manual work, especially in complex street scenes with various types of buildings. In this paper, we present a new and effective framework for extracting 2-D facade footprints from mobile LiDAR point clouds. The proposed framework consists of three steps: 1) line segment extraction from projected point clouds based on a hypotheses and selection strategy; 2) completion of missing parts between adjacent walls using line intersections; and 3) delineation of footprints through finding the least cost path in the graph of the line segments. We compare our method with several existing ones and discuss its robustness against data missing and noise such as nonwall structures and vegetation. Our proposed method is also tested in two large-scale data sets, a residential data set, and an urban data set. The coverage ratio, i.e., the percentage of outer wall points covered by the generated outlines in the residential data set is 93.4% and 91.7% in the urban data set is achieved. The mean distance between points of ground truth and constructed footprints for the residential data set and urban data set is 0.019 and 0.028 m, respectively. The experimental results demonstrate that the proposed framework is effective in modeling various facade footprints from mobile LiDAR point clouds.

Point cloud saliency detection via local sparse coding

The human visual system (HVS) can process large quantities of visual information instantly. Visual saliency perception is the process of locating and identifying regions with a high degree of saliency from a visual standpoint. Mesh saliency detection has been studied extensively in recent years, but few studies have focused on 3D point cloud saliency detection. The estimation of visual saliency is important for computer graphics tasks such as simplification, segmentation, shape matching and resizing. In this paper, we present a method for the direct detection of saliency on unorganized point clouds. First, our method computes a set of overlapping neighborhoods and estimates adescriptor vector for each point inside it. Then, the descriptor vectors are used as a natural dictionary in order to apply a sparse coding process. Finally, we estimate a saliency map of the point neighborhoods based on the Minimum Description Length (MDL) principle.Experiment results show that the proposed method achieves similar results to those from the literature review and in some cases even improves on them. It captures the geometry of the point clouds without using any topological information and achieves an acceptable performance. The effectiveness and robustness of our approach are shown by comparing it to previous studies in the literature review.

Robust point cloud normal estimation via neighborhood reconstruction

This article presents a novel method for estimating normals on unorganized point clouds that preserves sharp features. Many existing methods are unable to reliably estimate normals for points around sharp features since the neighborhood employed for the normal estimation would enclose points belonging to different surface patches across the sharp feature. To address this challenging issue, a neighborhood reconstruction-based normal estimation method is developed to find a proper neighborhood for points around sharp features. A robust statistics-based method is proposed to identify points that near sharp features and classify the points into two categories: edge points and non-edge points. Two specific neighborhood reconstruction strategies are designed for these two types of points to generate a neighborhood clear of sharp features. The normal of the current point can thus be reliably estimated by principal component analysis using the generated isotropic neighborhood. Numerous case studies have been carried out to compare the reliability and robustness of the proposed method against various existing methods. The experiment results show that the proposed approach performs better than the state-of-the-art methods most of the time and offers an ideal compromise between precision, speed, and robustness.

Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits.

We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the quadric surfaces for encapsulating the basic building blocks of our environments - planes, spheres, ellipsoids, cones or cylinders, in a unified fashion. Moreover, quadrics allow us to model higher degree of freedom shapes, such as hyperboloids or paraboloids that could be used in non-rigid settings. We begin by contributing two novel quadric fits targeting 3D point sets that are endowed with tangent space information. Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points. The second fit approximates the first, and reduces the computational effort. We theoretically analyze these fits with rigor, and give algebraic and geometric arguments. Next, by re-parameterizing the solution, we devise a new local Hough voting scheme on the null-space coefficients that is combined with RANSAC, reducing the complexity from O(N4) to O(N3) (three points). To the best of our knowledge, this is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes without segmentation. Our extensive qualitative and quantitative results show that our method is efficient and flexible, as well as being accurate.

AUTOMATED DETECTION AND LAYOUT REGULARIZATION OF SIMILAR FEATURES IN INDOOR POINT CLOUD

Abstract. Automated identification of high-level structures in unorganized point cloud of indoor spaces Indoor space is an important aspect of scene analysis that provides essential information for many applications, such as building digitization, indoor navigation and evacuation route planning. In addition, detection of repetition and regularities in the organization indoor environments, such as rooms, can be used to provide a contextual relationship in the reconstruction phase. However, retrieving high-level information is a challenging task due to the unorganized nature of the raw data, poor-quality of the input data that are in many cases contaminated with noise and outliers. in point benefit from the apparent regularities and strong contextual relationships in façades. The main observation exploited in this paper is the fact that building indoor is generally constituted by a set of basic shapes repeated several times in regular layouts. Building elements can be considered as similar if they share a set of features and elements in an idealized layout exhibiting some regularities. Starting from this main assumption a recursive adaptive partitioning of the indoor point cloud is carried out to automatically derive a flexible and hierarchical 3D representation of the building space. The presented methodology is tested on a synthetic dataset with Gaussian noise. The reconstructed pattern shows a close correspondence with the synthetic one showing the viability of the proposed approach.

3D Point Cloud Retrieval With Bidirectional Feature Match

In the recent decade, the development of 3D scanners brings the expansion of 3D models, which yields in the increase of demand for developing effective 3D point cloud retrieval methods using only unorganized point clouds instead of mesh data. In this paper, we propose a meshing-free framework for point cloud retrieval by exploiting a bidirectional similarity measurement on local features. Specifically, we first introduce an effective pipeline for keypoint selection by applying principal component analysis to pose normalization and thresholding local similarity of normals. Then, a point cloud based feature descriptor is employed to compute local feature descriptors directly from point clouds. Finally, we propose a bidirectional feature match strategy to handle the similarity measure. Experimental evaluation on a publicly available benchmark demonstrates the effectiveness of our framework and shows it can outperform other alternatives involving state-of-the-art techniques.

An Estimation Method for Normal of Unorganized Point Cloud Based on Local Isomorphic Surface

An Estimation Method for Normal of Unorganized Point Cloud Based on Local Isomorphic Surface

Learning to reconstruct 3D structures for occupancy mapping from depth and color information

Real-world scenarios contain many structural patterns that, if appropriately extracted and modeled, can be used to reduce problems associated with sensor failure and occlusions while improving planning methods in such tasks as navigation and grasping. This paper devises a novel unsupervised procedure that models 3D structures from unorganized pointclouds as occupancy maps. Our methodology enables the learning of unique and arbitrarily complex features using a variational Bayesian convolutional auto-encoder, which compresses local information into a latent low-dimensional representation and then decodes it back in order to reconstruct the original scene, including color information when available. This reconstructive model is trained on features obtained automatically from a wide variety of scenarios, in order to improve its generalization and interpolative powers. We show that the proposed framework is able to recover partially missing structures and reason over occlusions with high accuracy while maintaining a detailed reconstruction of observed areas. To combine localized feature estimates seamlessly into a single global structure, we employ the Hilbert maps framework, recently proposed as a robust and efficient occupancy mapping technique, and introduce a new kernel for reproducing kernel Hilbert space projection that uses estimates from the reconstructive model. Experimental tests are conducted with large-scale 2D and 3D datasets, using both laser and monocular data, and a study of the impact of various accuracy–speed trade-offs is provided to assess the limits of the proposed methodology.

Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds

Terrestrial LiDAR data can be used to extract accurate structure parameters of corn plant and canopy, such as leaf area, leaf distribution, and 3D model. The first step of these applications is to extract corn leaf points from unorganized LiDAR point clouds. This paper focused on an automated extraction algorithm for identifying the points returning on corn leaf from massive, unorganized LiDAR point clouds. In order to mine the distinct geometry of corn leaves and stalk, the Difference of Normal (DoN) method was proposed to extract corn leaf points. Firstly, the normals of corn leaf surface for all points were estimated on multiple scales. Secondly, the directional ambiguity of the normals was eliminated to obtain the same normal direction for the same leaf distribution. Finally, the DoN was computed and the computed DoN results on the optimal scale were used to extract leave points. The quantitative accuracy assessment showed that the overall accuracy was 94.10%, commission error was 5.89%, and omission error was 18.65%. The results indicate that the proposed method is effective and the corn leaf points can be extracted automatically from massive, unorganized terrestrial LiDAR point clouds using the proposed DoN method. Keywords: corn leaves, terrestrial LiDAR, cloud points, automatic extraction, crop growth monitoring, phenotyping, difference of normal (DoN), directional ambiguity of the normals DOI: 10.25165/j.ijabe.20181103.3177 Citation: Su W, Zhang M Z, Liu J M. Automated extraction of corn leaf points from unorganized terrestrial LiDAR point clouds. Int J Agric & Biol Eng, 2018; 11(3): 166–170.

POLE-LIKE ROAD FURNITURE DETECTION IN SPARSE AND UNEVENLY DISTRIBUTED MOBILE LASER SCANNING DATA

Abstract. Pole-like road furniture detection received much attention due to its traffic functionality in recent years. In this paper, we develop a framework to detect pole-like road furniture from sparse mobile laser scanning data. The framework is carried out in four steps. The unorganised point cloud is first partitioned. Then above ground points are clustered and roughly classified after removing ground points. A slicing check in combination with cylinder masking is proposed to extract pole-like road furniture candidates. Pole-like road furniture are obtained after occlusion analysis in the last stage. The average completeness and correctness of pole-like road furniture in sparse and unevenly distributed mobile laser scanning data was above 0.83. It is comparable to the state of art in the field of pole-like road furniture detection in mobile laser scanning data of good quality and is potentially of practical use in the processing of point clouds collected by autonomous driving platforms.

Surface reconstruction of incomplete datasets: A novel Poisson surface approach based on CSRBF

This paper introduces a novel surface reconstruction method based on unorganized point clouds, which focuses on offering complete and closed mesh models of partially sampled object surfaces. To accomplish this task, our approach builds upon a known a priori model that coarsely describes the scanned object to guide the modeling of the shape based on heavily occluded point clouds. In the region of space visible to the scanner, we retrieve the surface by following the resolution of a Poisson problem: the surface is modeled as the zero level-set of an implicit function whose gradient is the closest to the vector field induced by the 3D sample normals. In the occluded region of space, we consider the a priori model as a sufficiently accurate descriptor of the shape. Both models, which are expressed in the same basis of compactly supported radial functions to ensure computation and memory efficiency, are then blended to obtain a closed model of the scanned object. Our method is finally tested on traditional testing datasets to assess its accuracy and on simulated terrestrial LiDAR scanning (TLS) point clouds of trees to assess its ability to handle complex shapes with occlusions.

Robust normal estimation in unstructured 3D point clouds by selective normal space exploration

We present a fast and practical approach for estimating robust normal vectors in unorganized point clouds. Our proposed technique is robust to noise and outliers and can preserve sharp features in the input model while being significantly faster than the current state-of-the-art alternatives. The key idea to this is a novel strategy for the exploration of the normal space: First, an initial candidate normal vector, optimal under a robust least median norm, is selected from a discrete subregion of this space, chosen conservatively to include the correct normal; then, the final robust normal is computed, using a simple, robust procedure that iteratively refines the candidate normal initially selected. This strategy allows us to reduce the computation time significantly with respect to other methods based on sampling consensus and yet produces very reliable normals even in the presence of noise and outliers as well as along sharp features. The validity of our approach is confirmed by an extensive testing on both synthetic and real-world data and by a comparison against the most relevant state-of-the-art approaches.

Refinement of LiDAR point clouds using a super voxel based approach

We propose a new approach for automatic refinement of unorganized point clouds captured by LiDAR scanning systems. Given a point cloud, our method first abstracts the input data into super voxels via over segmentations, and then builds a K-nearest neighbor graph on these voxel nodes. Abstracting into voxel representation provides a means to generate an elastic wireframe over the original data. An iterative resampling method is then introduced to project resampling points to all potential surfaces considering repulsion constraints from both interior and exterior of voxels. Our point consolidation process contributes to accurate normal estimation, uniform point distribution, and sufficient sampling density. Experiments and comparisons have demonstrated that the proposed method is effective on point clouds from a variety of datasets.

Large-scale urban point cloud labeling and reconstruction

The large number of object categories and many overlapping or closely neighboring objects in large-scale urban scenes pose great challenges in point cloud classification. In this paper, a novel framework is proposed for classification and reconstruction of airborne laser scanning point cloud data. To label point clouds, we present a rectified linear units neural network named ReLu-NN where the rectified linear units (ReLu) instead of the traditional sigmoid are taken as the activation function in order to speed up the convergence. Since the features of the point cloud are sparse, we reduce the number of neurons by the dropout to avoid over-fitting of the training process. The set of feature descriptors for each 3D point is encoded through self-taught learning, and forms a discriminative feature representation which is taken as the input of the ReLu-NN. The segmented building points are consolidated through an edge-aware point set resampling algorithm, and then they are reconstructed into 3D lightweight models using the 2.5D contouring method (Zhou and Neumann, 2010). Compared with deep learning approaches, the ReLu-NN introduced can easily classify unorganized point clouds without rasterizing the data, and it does not need a large number of training samples. Most of the parameters in the network are learned, and thus the intensive parameter tuning cost is significantly reduced. Experimental results on various datasets demonstrate that the proposed framework achieves better performance than other related algorithms in terms of classification accuracy and reconstruction quality.

Multi-scan segmentation of terrestrial laser scanning data based on normal variation analysis

Point cloud segmentation groups points with similar attributes with respect to geometric, colormetric, radiometric, and/or other information to support Terrestrial Laser Scanning (TLS) data processing such as feature extraction, classification, modeling, analysis, and so forth. In this paper we propose a segmentation method consisting of two main steps. First, a novel feature extraction approach, NORmal VAriation ANAlysis (Norvana), eliminates some noise points and extracts edge points without requiring a general (and error prone) normal estimation at each point. Second, region growing groups the points on a smooth surface to obtain the segmentation result. For efficiency, both steps exploit the angular grid structure storing each TLS scan that is often neglected in many segmentation algorithms, which are primarily developed for unorganized point clouds. Additionally, unlike the existing methods exploiting the angular grid structure that can only be applied to a single scan, the proposed method is able to segment multiple registered scans simultaneously. The algorithm also takes advantage of parallel programming for efficiency. In the experiment, both qualitative and quantitative evaluations are performed through two datasets whilst the robustness and efficiency of the proposed method are analyzed and discussed.

Hough Transform for real-time plane detection in depth images

The automatic detection of planes in depth images plays an important role in computer vision. Plane detection from unorganized point clouds usually requires complex data structures to pre-organize the points. On the other hand, existing detection approaches tailored to depth images use the structure of the image and the 2.5-D projection of the scene to simplify the task. However, they are sensitive to noise and to discontinuities caused by occlusion. We present a real-time deterministic technique for plane detection in depth images that uses an implicit quadtree to identify clusters of approximately coplanar points in the 2.5-D space. The detection is performed by an efficient Hough-transform voting scheme that models the uncertainty associated with the best-fitting plane with respect to each cluster as a Gaussian distribution. Experiments shows that our approach is fast, scalable, and robust even in the presence of noise, partial occlusion, and discontinuities.

Efficient 3D Volume Reconstruction from a Point Cloud Using a Phase-Field Method

We propose an explicit hybrid numerical method for the efficient 3D volume reconstruction from unorganized point clouds using a phase-field method. The proposed three-dimensional volume reconstruction algorithm is based on the 3D binary image segmentation method. First, we define a narrow band domain embedding the unorganized point cloud and an edge indicating function. Second, we define a good initial phase-field function which speeds up the computation significantly. Third, we use a recently developed explicit hybrid numerical method for solving the three-dimensional image segmentation model to obtain efficient volume reconstruction from point cloud data. In order to demonstrate the practical applicability of the proposed method, we perform various numerical experiments.

采用八叉树体素生长的点云平面提取

采用八叉树体素生长的点云平面提取

Random walks for unorganized point cloud segmentation with application to aerospace repair

The ability to quickly and accurately segment surface defects from 3D scan data could help improve repeatability in the repair of high-value aerospace components by MRO firms. Many existing approaches to 3D point cloud segmentation, such as region growing algorithms, are not capable of differentiating the subtle changes in surface properties that often mark a defect boundary. The interactive, graph-based ‘random walker’ image segmentation algorithm has been adapted to segment a range of surface defects directly from raw, unorganized 3D scans of aerospace surfaces. Experiments with synthetic data show the algorithm delivers accurate results over a wide range of boundary conditions and on data with large amounts of simulated scanner noise.