Curve Skeleton Research Articles

Skeleton-based coordinate system construction method for non-cooperative targets

Due to the lack of structure and part information in non-cooperative targets, the task of pose estimation suffers from unstable and unreliable initial coordinate system. In this paper, we introduce a curve-skeleton-based method to construct the robust coordinate system automatically for non-cooperative targets. First, a nonmanifold-Laplacian-based method is proposed to extract the curve skeleton from point clouds. The proposed method solves the over-contraction problem while preserves the topology and structure of skeleton well during point clouds contraction. Additionally, we create the skeleton graph to detect the skeleton structure and key points automatically. Then, the detected skeleton feature is used to construct target coordinate system, and improve the stability and robustness. Specifically, a real-scan dataset is constructed in this paper, and the proposed point cloud contraction method outperforms previous methods in structural representation and visualization. The mean rotation error of proposed coordinate system construct method performs 0.1249° and 1.2135° in 0%–30% noise ratio of distortions and outliers, and performs 7.04° and 4.21° in real-scan aircraft and satellite datasets. The results in complete and incomplete datasets verify the robustness and reliability of our skeleton-based coordinate system.

Slicing-guided Skeleton Extraction Method for 3D Point Clouds of Human Body

<p>In the paper, a slicing-guided method is introduced to extract the curve skeleton from the point cloud body model. Firstly, the dominant eigenvector of body model as slicing direction is chosen adaptively, and the input body model is sliced accordingly. each slice is projected and classified into different regions, and the centroid of each region can be considered as initial skeleton point. Then, those skeleton points are removed outside models, and initial skeleton lines are generated by connecting points based on different region of body model. Finally, the two-step post-processing approach is proposed to improve the initial skeleton results for accurate topological analysis. With the branch point merging strategy, the initial skeleton of the model is optimized. Furthermore, the skeleton lines by interpolation optimization are refined and smoothed. Compared with similar skeleton extraction algorithms, the method proposed in the paper has relatively strong robustness and effectiveness, and can be applied to human body model in point cloud data.</p> <p> </p>

EPCS: Endpoint-based part-aware curve skeleton extraction for low-quality point clouds

The curve skeleton is an important shape descriptor which has been utilized in various applications in computer graphics, machine vision, and artificial intelligence. In this study, the endpoint-based part-aware curve skeleton (EPCS) extraction method for low-quality point clouds is proposed. The novel random center shift (RCS) method is first proposed for detecting the endpoints on point clouds. The endpoints are used as the initial seed points for dividing each part into layers, and then the skeletal points are obtained by computing the center points of the oriented bounding box (OBB) of the layers. Subsequently, the skeletal points are connected, thus forming the branches. Furthermore, the multi-vector momentum-driven (MVMD) method is also proposed for locating the junction points which connect the branches. Due to the shape differences between different parts on point clouds, the global topology of the skeleton is finally optimized by removing the redundant junction points, re-connecting some branches using the proposed MVMD method, and applying an interpolation method based on the splitting operator. Consequently, a complete and smooth curve skeleton is achieved. The proposed EPCS method is compared with several state-of-the-art methods, and the experimental results verify its robustness and effectiveness. Furthermore, the skeleton extraction and model segmentation results on challenging point clouds of broken Terracotta also highlight the utility of the proposed method.

Robustly Extracting Concise 3D Curve Skeletons by Enhancing the Capture of Prominent Features.

Extracting concise 3D curve skeletons with existing methods is still a serious challenge as these methods require tedious parameter adjustment to suppress the influence of shape boundary perturbations to avoid spurious branches. In this paper, we address this challenge by enhancing the capture of prominent features and using them for skeleton extraction, motivated by the observation that the shape is mainly represented by prominent features. Our method takes the medial mesh of the shape as input, which can maintain the shape topology well. We develop a series of novel measures for simplifying and contracting the medial mesh to capture prominent features and represent them concisely, by which means the influences of shape boundary perturbations on skeleton extraction are suppressed and the quantity of data needed for skeleton extraction is significantly reduced. As a result, we can robustly and concisely extract the curve skeleton based on prominent features, avoiding the trouble of tuning parameters and saving computations, as shown by experimental results.

Automated Detection and Characterization of Cracks on Concrete Using Laser Scanning

Accurate crack detection and characterization on concrete are essential for the maintenance, safety, and serviceability of various infrastructures. In this paper, an innovative approach was developed to automatically measure the cracks from 3D point clouds collected by a phase-shift terrestrial laser scanner (TLS) (FARO Focus3D S120). The approach integrates several techniques to characterize the cracks, which include the deviation on point normal determined using k-nearest neighbor (kNN) and principal components analysis (PCA) algorithms to identify the cracks, and principal axes and curve skeletons of cracks to determine the projected and real dimensions of cracks, respectively. The coordinate transformation was then performed to estimate the projected dimensions of cracks. Curve skeletons and cross sections of cracks were extracted to represent the real dimensions. Two cases of surface cracks were used to validate the developed approach. Because of the differences in definitions of the crack dimension in the three methods and due to the curve shape of the crack, the width and depth of cracks obtained from the cross-section method and manual measurement were close but slightly smaller than those measured by the projection algorithm; whereas the length of cracks determined by the curve-skeletons method was slightly larger than those obtained by the manual measurement and projection method. The real dimension of a crack has good agreements with real situations when compared with the results of the manual measurement and projection method.

Out‐of‐core Extraction of Curve Skeletons for Large Volumetric Models

AbstractExisting methods for skeleton extraction have limitations in terms of the amount of memory space available, as the model must be allocated to the random access memory. This challenges the treatment of out‐of‐core models. Although applying out‐of‐core simplification methods to the model can fit in memory, this would induce distortion of the model surface, and so causing the skeleton to be off‐centered or changing the topological structure. In this paper, we propose an efficient out‐of‐core method for extracting skeletons from large volumetric models. The method takes a volumetric model as input and first computes an out‐of‐core distance transform. With the distance transform, we generate a medial mesh to capture the prominent features for skeleton extraction, which significantly reduces the data size and facilitates the process of large models. At last, we contract the medial mesh in an out‐of‐core fashion to generate the skeleton. Experimental results show that our method can efficiently extract high‐quality curve skeletons from large volumetric models with small memory usage.

Curve Skeleton Extraction from Incomplete Point Clouds of Livestock and Its Application in Posture Evaluation

As consumer-grade depth sensors provide an efficient and low-cost way to obtain point cloud data, an increasing number of applications regarding the acquisition and processing of livestock point clouds have been proposed. Curve skeletons are abstract representations of 3D data, and they have great potential for the analysis and understanding of livestock point clouds. Articulated skeleton extraction has been extensively studied on 2D and 3D data. Nevertheless, robust and accurate skeleton extraction from point set sequences captured by consumer-grade depth cameras remains challenging since such data are often corrupted by substantial noise and outliers. Additionally, few approaches have been proposed to overcome this problem. In this paper, we present a novel curve skeleton extraction method for point clouds of four-legged animals. First, the 2D top view of the livestock was constructed using the concave hull algorithm. The livestock data were divided into the left and right sides along the bilateral symmetry plane of the livestock. Then, the corresponding 2D side views were constructed. Second, discrete skeleton evolution (DSE) was utilized to extract the skeletons from those 2D views. Finally, we divided the extracted skeletons into torso branches and leg branches. We translated each leg skeleton point to the border of the nearest banded point cluster and then moved it to the approximate centre of the leg. The torso skeleton points were calculated according to their positions on the side view and top view. Extensive experiments show that quality curve skeletons can be extracted from many livestock species. Additionally, we compared our method with representative skeleton extraction approaches, and the results show that our method performs better in avoiding topological errors caused by the shape characteristics of livestock. Furthermore, we demonstrated the effectiveness of our extracted skeleton in detecting frames containing pigs with correct postures from the point cloud stream.

Truss Parametrization of Topology Optimization Results with Curve Skeletons and Meta Balls

AbstractTruss-like shapes can occur in topology optimization described by an assembly of finite elements or its boundary represented as a polygon mesh. Such shape description does not cover a common engineering parametrization like the lines of a frame structure and its corresponding cross-section. This article addresses the truss-parametrization of such optimization using curve skeletons and Meta Balls. While the curve skeleton is common in the truss-parametrization, including Meta Balls can lead to an overall implicit and smooth shape description.

Skeletonization via Local Separators

We propose a new algorithm for curve skeleton computation that differs from previous algorithms by being based on the notion of local separators . The main benefits of this approach are that it is able to capture relatively fine details and that it works robustly on a range of shape representations. Specifically, our method works on shape representations that can be construed as spatially embedded graphs. Such representations include meshes, volumetric shapes, and graphs computed from point clouds. We describe a simple pipeline where geometric data are initially converted to a graph, optionally simplified, local separators are computed and selected, and finally a skeleton is constructed. We test our pipeline on polygonal meshes, volumetric shapes, and point clouds. Finally, we compare our results to other methods for skeletonization according to performance and quality.

Distance-Based Skeletonization on the BCC Grid

Strand proposed a distance-based thinning algorithm for computing surface skeletons on the body-centered cubic (BCC) grid. In this paper, we present two modified versions of this algorithm that are faster than the original one, and less sensitive to the visiting order of points in the sequential thinning phase. In addition, a novel algorithm capable of producing curve skeletons is also reported.

3D DP Improved Algorithm Based on Bending Hierarchy Structure Identification

3D Douglas-Peucker (DP) algorithm is an effective 3D discrete point data synthesis algorithm. In view of over-synthesis of compound bending during simplification of 3D DP algorithm with additional bending adjustment index, this paper designs a bending hierarchical structured algorithm to improve the original algorithm. Bending convexity threshold is introduced to merge the bending operation, curve skeletons with different scales are selected through statistics of bending height distribution. Bending adjustment index on different hierarchies of bending skeleton is calculated for synchronous synthesis between line features and DEM. The experimental results show that the structuring process well retains the original details of the curve, clearly presents the overall curve trend with obvious simplification effect, thus solving the problem of over-synthesis of compound bending, so that the curve displays different bending hierarchy structures. The simplification result demonstrates completeness of geographical significance and similarity between the graphics before and after the simplification.

Cylindrical Shape Decomposition for 3D Segmentation of Tubular Objects

We develop a cylindrical shape decomposition (CSD) algorithm to decompose an object, a union of several tubular structures, into its semantic components. We decompose the object using its curve skeleton and restricted translational sweeps. For that, CSD partitions the curve skeleton into maximal-length sub-skeletons over an orientation cost, each sub-skeleton corresponds to a semantic component. To find the intersection of the tubular components, CSD translationally sweeps the object in decomposition intervals to identify critical points at which the shape of the object changes substantially. CSD cuts the object at critical points and assigns the same label to parts along the same sub-skeleton, thereby constructing a semantic component. The proposed method further reconstructs the acquired semantic components at the intersection of object parts using generalized cylinders. We apply CSD for segmenting axons in large 3D electron microscopy images and decomposing vascular networks and synthetic objects. We show that our proposal is robust to severe surface noise and outperforms state-of-the-art decomposition techniques in its applications.

Random generation of convex aggregates for DEM study of particle shape effect

In this study, the random generation of 3D convex aggregates is proposed for the numerical study of the particle shape effect in DEM simulation. First, the Bezier curve fitting technique is employed to construct a curve skeleton inside a randomly generated polyhedron. Then, a solid surface is generated based on the curve skeleton using the Gregory surface interpolation algorithm. Next, convex aggregates are created inside both regular and irregular polyhedrons to demonstrate the capability of the proposed algorithm. Finally, DEM simulation of convex aggregates with different roundness features is performed to study the effect of roundness on the shear behavior of granular aggregates.

Visibility-driven skeleton extraction from unstructured points

We present an algorithm for curve skeleton extraction from unstructured points using visibility as a guide. We introduce visible cells inside the point samples. By locating a viewpoint in the space, visible points from the viewpoint are connected to indicate the visible region, which locally captures the interior structure of the points and is called a visible cell. We then analyze and clean the visible cells for noisy and incomplete points. By connecting the cleaned visible cells with a region growing method, we obtain the skeleton along which the visibility of the unstructured points is balanced. No normal computation or ellipse fitting is required by using the proposed method. We show that the proposed skeleton well represents the shape. We experiment with our algorithm on imperfect points and show that comparative curved skeletons are extracted with relative lower computational cost.

A Simple and Stable Centeredness Measure for 3D Curve Skeleton Extraction.

Existing methods for extracting 3D curve skeletons mostly suffer from the difficulty of finding the center points of 3D shapes and tedious manual adjustments of the thresholds for pruning spurious branches due to the influence of shape boundary perturbations. In this article, we present a method based on medial surfaces of 3D shapes for the convenient and fast extraction of high-quality curve skeletons. Our main contribution is a simple and stable centeredness measure. It is based on simulating fire propagation via the scheme of inside-out evolution from the interior to the boundary, differentiating it from existing methods that use the scheme of outside-in evolution from the boundary to the interior. Thus, our measure is much more localized, and it can be implemented with a high degree of parallelism. In addition, we propose measures to effectively suppress the influence of details to obtain a stable measurement, and employ minimum set covers to optimize the center points to generate compact skeletons, which enables spurious branches to be effectively excluded without the tedious work of manually adjusting thresholds. Our experiments show the superiority of our method over existing methods, including its convenient generation of clean, compact and centered curve skeletons while running much faster than state-of-the-art methods.

Automated Analysis of Brain Microvasculature: From Segmentation to Anatomical Modeling.

Two-photon microscopy (TPM) can provide a detailed microscopic information of cerebrovascular structures. Extracting anatomical vascular models from TPM angiograms remains a tedious task due to image degeneration associated with TPM acquisitions and the complexity of microvascular networks. Here, we propose a fully automated pipeline capable of providing useful anatomical models of vascular structures captured with TPM. In the proposed method, we segment blood vessels using a fully convolutional neural network and employ the resulting binary labels to create an initial geometric graph enclosed within vessels boundaries. The initial geometry is then decimated and refined to form graphed curve skeletons that can retain both the vascular shape and its topology. We validate the proposed method on 3D realistic TPM angiographies and compare our results with that obtained through manual annotations.

Co-skeletons: Consistent curve skeletons for shape families

We present co-skeletons, a new method that computes consistent curve skeletons for 3D shapes from a given family. We compute co-skeletons in terms of sampling density and semantic relevance, while preserving the desired characteristics of traditional, per-shape curve skeletonization approaches. We take the curve skeletons extracted by traditional approaches for all shapes from a family as input, and compute semantic correlation information of individual skeleton branches to guide an edge-pruning process via skeleton-based descriptors, clustering, and a voting algorithm. Our approach achieves more concise and family-consistent skeletons when compared to traditional per-shape methods. We show the utility of our method by using co-skeletons for shape segmentation and shape blending on real-world data.

Skeletonization via dual of shape segmentation

Curve skeletons of 3D objects are central to many geometry analysis tasks in the field of computer graphics. A desirable skeleton has to meet at least four requirements: (1) topologically homotopic to the primitive shape, (2) truly well-centred, (3) feature preserving and (4) has a reasonable degree of smoothness. There are at least a couple of difficulties with skeletonization. On the one hand, finding the “best” skeleton is related to visual perception, to some extent, and thus hard to be completely solved by a pure geometric technique. On the other hand, how to exactly characterize the centredness of a skeleton, without a pre-computed medial axis surface, still remains challenging.Due to the fact that skeletons are able to encode the overall structure, a skeleton has been used to guide segmentation of a shape, which implies that there exists a dual relationship between segmentation and skeletonization. Based on the underlying duality, we propose to generate skeletons from a reliable segmentation result that is more easily available by deep learning or alternative techniques. In implementation, we first extract a collection of samples and then compute the Voronoi diagram restricted in the volume w.r.t. those samples, followed by transforming the clipped Voronoi diagram into a graph G. We further equip each edge in G with a centredness score. The user-specific segmentation result is then used to decompose G into a set of subgraphs Gi=1k. The next task is to compute the Steiner tree for each subgraph while requiring that the Steiner trees of two adjacent parts Gi and Gj must be linked together. The global structure of the final skeleton inherits the proximity configuration of the user-specific segmentation, and thus is topologically homotopic to the primitive shape. At the same time, the centredness of the final skeleton is taken into full consideration by maximizing the overall centredness score. We also integrate the other two requirements carefully into our algorithmic framework. We conduct extensive experiments to evaluate the new approach in terms of the above-mentioned aspects. The experimental results show that our approach has an obvious advantage over the state-of-the-arts. As a by-product of our algorithm, users can obtain skeletons with different levels of details by editing the segmentation configurations.

Curve Skeleton Extraction From 3D Point Clouds Through Hybrid Feature Point Shifting and Clustering

AbstractCurve skeleton is an important shape descriptor with many potential applications in computer graphics, visualization and machine intelligence. We present a curve skeleton expression based on the set of the cross‐section centroids from a point cloud model and propose a corresponding extraction approach. We first provide the substitution of a distance field for a 3D point cloud model, and then combine it with curvatures to capture hybrid feature points. By introducing relevant facets and points, we shift these hybrid feature points along the skeleton‐guided normal directions to approach local centroids, simplify them through a tensor‐based spectral clustering and finally connect them to form a primary connected curve skeleton. Furthermore, we refine the primary skeleton through pruning, trimming and smoothing. We compared our results with several state‐of‐the‐art algorithms including the rotational symmetry axis (ROSA) and L1‐medial methods for incomplete point cloud data to evaluate the effectiveness and accuracy of our method.

Curve skeleton extraction via k-nearest-neighbors based contraction

Curve skeleton extraction via k-nearest-neighbors based contraction