A Delaunay-based region-growing approach to surface reconstruction from unorganized points

This paper presents a Delaunay-based region-growing (DBRG) surface reconstruction algorithm that holds the advantages of both Delaunay-based and region-growing approaches. The proposed DBRG algorithm takes a set of unorganized sample points from the boundary surface of a three-dimensional object and produces an orientable manifold triangulated model with a correct geometry and topology that is faithful to the original object. Compared with the traditional Delaunay-based approach, the DBRG algorithm requires only one-pass Delaunay computation and needs no Voronoi information because it improves the non-trivial triangle extraction by using a region-growing technique. Compared with the traditional region-growing methods, the proposed DBRG algorithm makes the surface reconstruction more systematic and robust because it inherits the structural characteristics of the Delaunay triangulation, which nicely complements the absence of geometric information in a set of unorganized points. The proposed DBRG algorithm is capable of handling surfaces with complex topology, boundaries, and even non-uniform sample points. Experimental results show that it is highly efficient compared with other existing algorithms.     

Localized Cocone surface reconstruction

We introduce a surface reconstruction algorithm suitable for large point sets. The algorithm is an octree-based version of the Cocone reconstruction algorithm [4], allowing independent processing of small subsets of the total input point set. When the points are sufficiently sampled from a smooth surface, the global guarantee of topological correctness of the original algorithm is preserved, together with the geometric accuracy guarantees.     

Optimization-based approach for curve and surface reconstruction

This paper introduces an optimization-based approach for the curve reconstruction problem, where piecewise linear approximations are computed from sets of points sampled from target curves. In this approach, the problem is formulated as an optimization problem. To be more concrete, at first the Delaunay triangulation for the sample points is computed, and a weight is assigned with each Delaunay edge. Then the problem becomes minimization or maximization of the total weights of the edges that constitute the reconstruction. This paper proposes one exact method and two approximate methods, and shows that the obtained results are improved both theoretically and empirically. In addition, the optimization-based approach is further extended to three dimensions, where surfaces are to be reconstructed, and the quality of the reconstructions is examined.     

A greedy Delaunay-based surface reconstruction algorithm

In this paper, we present a new greedy algorithm for surface reconstruction from unorganized point sets. Starting from a seed facet, a piecewise linear surface is grown by adding Delaunay triangles one by one. The most plausible triangles are added first and in such a way as to prevent the appearance of topological singularities. The output is thus guaranteed to be a piecewise linear orientable manifold, possibly with boundary. Experiments show that this method is very fast and achieves topologically correct reconstruction in most cases. Moreover, it can handle surfaces with complex topology, boundaries, and nonuniform sampling.     

Manifold surface reconstruction of an environment from sparse Structure-from-Motion data

The majority of methods for the automatic surface reconstruction of an environment from an image sequence have two steps: Structure-from-Motion and dense stereo. From the computational standpoint, it would be interesting to avoid dense stereo and to generate a surface directly from the sparse cloud of 3D points and their visibility information provided by Structure-from-Motion. The previous attempts to solve this problem are currently very limited: the surface is non-manifold or has zero genus, the experiments are done on small scenes or objects using a few dozens of images. Our solution does not have these limitations. Furthermore, we experiment with hand-held or helmet-held catadioptric cameras moving in a city and generate 3D models such that the camera trajectory can be longer than one kilometer.     

3D reconstruction using silhouettes from unordered viewpoints

In this paper, we present a novel approach for reconstructing an object surface from its silhouettes. The proposed approach directly estimates the differential structure of the surface, and results in a higher accuracy than existing volumetric approaches for object reconstruction. Compared with other existing differential approaches, our approach produces relatively complete 3D models similar to volumetric approaches, with the topology conforming to what is observed from the silhouettes. In addition, the method neither assumes nor depends on the spatial order of viewpoints. Experimental results on both synthetic and real world data are presented, and comparison is made with other existing approaches to demonstrate the superiority of the proposed approach.     

Splat-based surface reconstruction from defect-laden point sets

We introduce a method for surface reconstruction from point sets that is able to cope with noise and outliers. First, a splat-based representation is computed from the point set. A robust local 3D RANSAC-based procedure is used to filter the point set for outliers, then a local jet surface – a low-degree surface approximation – is fitted to the inliers. Second, we extract the reconstructed surface in the form of a surface triangle mesh through Delaunay refinement. The Delaunay refinement meshing approach requires computing intersections between line segment queries and the surface to be meshed. In the present case, intersection queries are solved from the set of splats through a 1D RANSAC procedure.     

基于Delaunay剖分的心内膜表面动态三维重建算法

心内膜表面三维重建技术在三维标测系统起着手术导航和靶点定位的作用。针对心内科手术中实时采集的散乱心内膜点云,提出了一种基于Delaunay剖分的表面动态三维重建算法。以CGAL非递归方式实现的逐点插入计算Delaunay剖分算法为基础,在剖分过程中,用关联采样点的伞局部替换原来表面中不满足Gabriel准则表面面片的集合,心内膜表面结构随着点云Delaunay剖分的变化而进行动态地更新。同时为了有效地表达心内膜表面及其点云的Delaunay剖分,并能够快速地索引四面体网格和表面三角面片,提出了一种以vtkDataArray为基础的几何数据结构。最后,实验表明该方法在重建结果和重建时间上可以满足心内科手术中的临床应用。     

三维物体表面重建的分支处理算法研究

根据平面点集Delaunay三角剖分的特性,将Delaunay三角剖分应用到分支问题上,改进和实现了一种分支问题处理算法。将相邻层轮廓线投影到同一个剖面上形成一个带约束边的平面点集,并将它们Delaunay三角化,根据这些三角形组来生成新的轮廓线,使轮廓线一一对应。实验结果表明该算法实现的效果较符合实际情况,能有效地处理各种不同情况。     

基于快速Delaunay三角化的散乱点曲面重建算法

针对现有三维重建算法速度较慢的问题,提出了一种基于快速Delaunay三角化的散乱数据点的三维重建算法。首先,提出一种新的平面Delaunay三角化插入点目标三角形定位算法,利用插入点的方向搜索线与三角形是否相交以及交点个数加速目标三角形定位,不用额外判断点是否在三角形内;其次,自动检测曲面漏洞,利用凸壳的边界拼接方法进行漏洞弥补。实验结果表明,本算法不仅能较好地重建出三维模型,而且有较高的效率。     

Point reconstruction from noisy images

In this paper we treat the problem of determining optimally (in the least-squares sense) the 3D coordinates of a point, given its noisy images formed by any number of cameras of known geometry. The optimality criterion is determined by the covariance matrices associated with the images of the point. The covariance matrices are not restricted to be positive definite but are allowed to be singular. Thus, image points constrained to lie along straight lines can be handled as well. Estimation of the covariance of the reconstructed point is provided.The often appearing two-camera stereo case is treated in detail. It is shown in this case that, under reasonable conditions, the main step of the reconstruction reduces to finding the unique zero of a sixth degree polynomial in the interval (0, 1).The authors are listed in random order.     

Reconstruction of 2D polygonal curves and 3D triangular surfaces via clustering of Delaunay circles/spheres

A simple and efficient method is presented in this paper to reliably reconstruct 2D polygonal curves and 3D triangular surfaces from discrete points based on the respective clustering of Delaunay circles and spheres. A Delaunay circle is the circumcircle of a Delaunay triangle in the 2D space, and a Delaunay sphere is the circumsphere of a Delaunay tetrahedron in the 3D space. The basic concept of the presented method is that all the incident Delaunay circles/spheres of a point are supposed to be clustered into two groups along the original curve/surface with satisfactory point density. The required point density is considered equivalent to that of meeting the well-documented r-sampling condition. With the clustering of Delaunay circles/spheres at each point, an initial partial mesh can be generated. An extrapolation heuristic is then applied to reconstructing the remainder mesh, often around sharp corners. This leads to the unique benefit of the presented method that point density around sharp corners does not have to be infinite. Implementation results have shown that the presented method can correctly reconstruct 2D curves and 3D surfaces for known point cloud data sets employed in the literature.     

基于Delaunay三角化的噪声点云非均匀采样

针对基于Delaunay三角化曲面重建方法要求点云密度满足ε-sample条件,提出了一种基于Delaunay三角化的噪声点云非均匀采样算法。首先,利用k-邻近点的Voronoi顶点计算出各点的负极点来逼近曲面中轴(MA);然后,根据近似中轴估计出曲面局部特征尺度(LFS);最后,结合Bound Cocone算法,删除多余的非边界点。实例表明,该算法可以准确、稳健地简化噪声点云,同时可以很好地保留曲面边界特征,经简化后的点云适用于基于Delaunay三角化的曲面重建方法。     

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.     

A tolerant approach to reconstruct topology from unorganized trimmed surfaces

Presented in this paper is a topology reconstruction algorithm from a set of unorganized trimmed surfaces. Error-prone small geometric elements are handled to give proper topological information. It gives complete topology to topologically complete models, and it is also tolerant to incomplete models. The proposed algorithm is vertex-based in that clues for topological information are searched from the set of vertices first, not from that of edges.     

散乱数据点集曲线重构的最短路逼近算法

给出了散乱数据点集曲线重构的最短路逼近算法．算法根据数据点的分布构造带权连通图,通过求解带权连通图的最短路径,将散乱数据点集的曲线重构问题转化为有序数据点集的曲线重构问题．算法可以对单连通、多连通和封闭的数据点集进行重构．重构曲线较好地保持了数据点集的形状和走向,尤其是带尖点的数据点集的形状特征．最后给出不同拓扑结构的数据点集的重构曲线实例．