Parallel Surface Reconstruction for Particle‐Based Fluids

This paper presents a novel method that improves the efficiency of high‐quality surface reconstructions for particle‐based fluids using Marching Cubes. By constructing the scalar field only in a narrow band around the surface, the computational complexity and the memory consumption scale with the fluid surface instead of the volume. Furthermore, a parallel implementation of the method is proposed. The presented method works with various scalar field construction approaches. Experiments show that our method reconstructs high‐quality surface meshes efficiently even on single‐core CPUs. It scales nearly linearly on multi‐core CPUs and runs up to fifty times faster on GPUs compared to the original scalar field construction approaches.     

Providing Websites with capabilities of one–to–one marketing

A formidable synergy can be obtained by putting expert system technology into the Internet. The modern trend of embedding expert systems into Websites turns out to be very promising, in particular for the field of marketing via the Web. The last two years have seen a growing interest in providing Websites with suitable embedded expert systems for one–to–one marketing. One–to– one marketing means marketing in a personalized way, i.e. marketing in a way that is adaptive to the personal needs of the user. A basic feature of this marketing framework consists in personalized prioritizing of news, i.e. presenting information in an order that is relevant to the specific needs of the current user. If the personalized prioritizing of news is a very useful feature in wired Web, it becomes essential in wireless Web, the promising next generation of the Web.
The paper presents a general methodology for personalized prioritizing of news. The methodology integrates decision theory with a deep–knowledge–based user model (i.e. causal knowledge linking user preferences to user goals). The deep–knowledge model of the user is a source of power of the methodology because it allows the system to know (and possibly explain) why the user acts the way he/she acts. Another relevant aspect of the methodology is that the burden of personalization is not placed on the user, and in fact the user does not have to declare his/her needs or interests or goals: they are automatically inferred from his/her profile data. In order to investigate the ideas underlying the proposal, a methodology example has been implemented in a prototype and then tested on real cases in the context of a supercomputing portal.     

A Well-Balanced Reconstruction of Wet/Dry Fronts for the Shallow Water Equations

In this paper, we construct a well-balanced, positivity preserving finite volume scheme for the shallow water equations based on a continuous, piecewise linear discretization of the bottom topography. The main new technique is a special reconstruction of the flow variables in wet–dry cells, which is presented in this paper for the one dimensional case. We realize the new reconstruction in the framework of the second-order semi-discrete central-upwind scheme from (Kurganov and Petrova, Commun. Math. Sci., 5(1):133–160, 2007). The positivity of the computed water height is ensured following (Bollermann et al., Commun. Comput. Phys., 10:371–404, 2011): The outgoing fluxes are limited in case of draining cells.     

VASE: Volume‐Aware Surface Evolution for Surface Reconstruction from Incomplete Point Clouds

Objects with many concavities are difficult to acquire using laser scanners. The highly concave areas are hard to access by a scanner due to occlusions by other components of the object. The resulting point scan typically suffers from large amounts of missing data. Methods that use surface‐based priors rely on local surface estimates and perform well only when filling small holes. When the holes become large, the reconstruction problem becomes severely under‐constrained, which necessitates the use of additional reconstruction priors. In this paper, we introduce weak volumetric priors which assume that the volume of a shape varies smoothly and that each point cloud sample is visible from outside the shape. Specifically, the union of view‐rays given by the scanner implicitly carves the exterior volume, while volumetric smoothness regularizes the internal volume. We incorporate these priors into a surface evolution framework where a new energy term defined by volumetric smoothness is introduced to handle large amount of missing data. We demonstrate the effectiveness of our method on objects exhibiting deep concavities, and show its general applicability over a broader spectrum of geometric scenario.     

Topology-Aware Surface Reconstruction for Point Clouds

We present an approach to incorporate topological priors in the reconstruction of a surface from a point scan. We base the reconstruction on basis functions which are optimized to provide a good fit to the point scan while satisfying predefined topological constraints. We optimize the parameters of a model to obtain a likelihood function over the reconstruction domain. The topological constraints are captured by persistence diagrams which are incorporated within the optimization algorithm to promote the correct topology. The result is a novel topology-aware technique which can (i) weed out topological noise from point scans, and (ii) capture certain nuanced properties of the underlying shape which could otherwise be lost while performing surface reconstruction. We show results reconstructing shapes with multiple potential topologies, compare to other classical surface construction techniques, and show the completion of real scan data.     

基于Voxel编码的曲面重建

一个完整的基于轮廓的曲面重建方法必须建立轮廓对应、解决分支和三角面片的构建.然而大多数已有的曲面重建算法只能解决问题的某些方面,从而导致这些算法不能有效地运用到复杂曲面重建,比如用磁共振获取的人大脑序列切片等盘旋且多分支凸包数据.提出了一个基于Voxel(像素)编码技术的曲面重建算法,该算法能以一种完全自动的方式处理带有空洞的复杂多分支曲面.首先将两相邻断层轮廓投影到定位于中间的一个辅助平面上,求得其差区域,然后根据差区域的不同情形进行分组.对每组轮廓,从对应的差邻域中提取骨架,并用骨架来度量两轮廓的不相似量,对不相似的进行剪支分解,从而使不相似的、复杂的轮廓转换为简单且相似的骨架轮廓对,最后完成三角片构建.重建曲面由二维流体三角面片组成,且仅经过切片上的输入廓线.算法已用手工数据和复杂人脑皮层的磁共振数据进行了仿真测试,检验了算法的有效性.     

Bipartite Polar Classification for Surface Reconstruction

In this paper, we propose bipartite polar classification to augment an input unorganized point set ? with two disjoint groups of points distributed around the ambient space of ? to assist the task of surface reconstruction. The goal of bipartite polar classification is to obtain a space partitioning of ? by assigning pairs of Voronoi poles into two mutually invisible sets lying in the opposite sides of ? through direct point set visibility examination. Based on the observation that a pair of Voronoi poles are mutually invisible, spatial classification is accomplished by carving away visible exterior poles with their counterparts simultaneously determined as interior ones. By examining the conflicts of mutual invisibility, holes or boundaries can also be effectively detected, resulting in a hole‐aware space carving technique. With the classified poles, the task of surface reconstruction can be facilitated by more robust surface normal estimation with global consistent orientation and off‐surface point specification for variational implicit surface reconstruction. We demonstrate the ability of the bipartite polar classification to achieve robust and efficient space carving on unorganized point clouds with holes and complex topology and show its application to surface reconstruction.     

离散点重构车身曲面的一种方法

基于激光无触点式三坐标测量仪所测得的曲面离散数据点 ,论文给出了一种用双三次B样条曲面插值来重构车身曲面的方法。该方法首先介绍B样条曲线和曲面的插值算法 ,接着对曲面离散点进行预处理 ,最后对离散点进行曲面插值。论文所生成的曲面网格 ,不仅适用于车身曲面设计 ,而且能用作车身覆盖件冲压仿真计算有限元模型     

Distributed Poisson Surface Reconstruction

Screened Poisson surface reconstruction robustly creates meshes from oriented point sets. For large datasets, the technique requires hours of computation and significant memory. We present a method to parallelize and distribute this computation over multiple commodity client nodes. The method partitions space on one axis into adaptively sized slabs containing balanced subsets of points. Because the Poisson formulation involves a global system, the challenge is to maintain seamless consistency at the slab boundaries and obtain a reconstruction that is indistinguishable from the serial result. To this end, we express the reconstructed indicator function as a sum of a low-resolution term computed on a server and high-resolution terms computed on distributed clients. Using a client–server architecture, we map the computation onto a sequence of serial server tasks and parallel client tasks, separated by synchronization barriers. This architecture also enables low-memory evaluation on a single computer, albeit without speedup. We demonstrate a 700 million vertex reconstruction of the billion point David statue scan in less than 20 min on a 65-node cluster with a maximum memory usage of 45 GB/node, or in 14 h on a single node.     

面向心脏表面断层数据的三角网格重构方法

针对不同轮廓线提取方法对心脏断层图像提取的心脏表面轮廓线数据产生的格式不同,研究并设计了一种通用的心脏表面三角网格重构方法。利用心脏形态和断层数据的基本特点,将余弦值旋转排列和缓排序的方法融入三次剖分三角网格生成方法中,实现了同步进行轮廓线点间关系的恢复与心脏表面的三角网格重建。详细阐述了其基本原理、实现策略和算法设计,并以实验验证了方法的有效性。     

一种变形表面的三维测量方法

提出一种新的利用标定图像进行三维测量的方法。利用SIFT算法找到初始的对应点,然后根据这些点生成三维空间中的种子点,再以这些种子点为中心,向外区域增长,直到完成整个物体表面测量。在每次增长的过程中,需要计算增长的三维空间平面在两个相机上的投影之间的图像相关系数。图像相关系数较大时认为是正确的增长,否则是错误的增长。实验证明,使用该方法能够得到很好的三维测量结果。     

Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction

We present a method—termed Helmholtz stereopsis—for reconstructing the geometry of objects from a collection of images. Unlike existing methods for surface reconstruction (e.g., stereo vision, structure from motion, photometric stereopsis), Helmholtz stereopsis makes no assumptions about the nature of the bidirectional reflectance distribution functions (BRDFs) of objects. This new method of multinocular stereopsis exploits Helmholtz reciprocity by choosing pairs of light source and camera positions that guarantee that the ratio of the emitted radiance to the incident irradiance is the same for corresponding points in the two images. The method provides direct estimates of both depth and surface normals, and consequently weds the advantages of both conventional stereopsis and photometric stereopsis. Results from our implementation lend empirical support to our technique.     

Holographic Method for Reconstruction of Random Rough Surface

A holographic principle for computer reconstruction of random rough surface (RRS) and determination of the surface parameters is used. The random function describing the surface is expanded in a Fourier series. The scattering from the RRS light field is approximated as a field, obtained as result of diffraction from sinusoidal gratings. The mixed field (obtained from the mixing of the diffracted field and the field of the laser beam) carries information about the phase of each sinusoidal grating. Sections of the diffracted and mixing fields are correspondingly registered by photodiode array. The first, the second and the third diffraction orders from each grating are taken into account for the surface reconstruction. The statistical distribution and the autocorrelation function of the surface height fluctuation are calculated. The surface parameters of mean roughness, root mean square and correlation length are determined. The studied surface, having a Gaussian statistic, is measured [from the work of Daintyet al.Talystep profilometer. The values of the above-mentioned surface parameters calculated in Daintyet al.are in good agreement with those obtained in the present work.     

Image Interpolation Through Surface Reconstruction

Reconstructing an HR （high-resolution） image which preserves the image intrinsic structures from its LR （low-resolution） counterpart is highly challenging. This paper proposes a new surface reconstruction algorithm applied to image interpolation. The interpolation surface for the whole image is generated by putting all the quadratic polynomial patches together. In order to eliminate the jaggies of the edge, a new weight fimction containing edge information is incorporated into the patch reconstruction procedure as a constraint. Extensive experimental results demonstrate that our method produces better results across a wide range of scenes in terms of both quantitative evaluation and subjective visual quality.     

Surface Reconstruction Using Alpha Shapes

We describe a method for reconstructing an unknown surface from a set of data points. The basic approach is to extract the surface as a polygon mesh from an α-shape. Even though alpha shapes are generalized polytopes having complicated internal structures, we show that manifold surfaces, with or without boundaries, can be efficiently generated, and these surfaces completely describe the α-shapes to the extent that they are visible from outside. Unlike the original α-shapes, the polygonal surfaces can be easily simplified to yield compact models suitable for a variety of geometric modeling applications such as surface fitting.     

分片驱动的特征敏感曲面重建

为健壮处理包含尖锐特征或欠采样的数据点集,通过对基于边界推进曲面重建技术的扩展,提出一种分片驱动的、特征敏感的对无方向散乱数据点集进行曲面重建的算法.在一个光滑阈值的控制下,将曲面重建过程分成分片重建和特征缝合2个阶段.在分片重建中,从光滑的种子三角化区域开始进行边界推进三角化,并通过拓扑元素分类与特征检测对边界光顺和特征重定位,以进一步扩展该分片,重复该过程,得到对光滑区域三角化的一系列光滑分片;特征缝合阶段,在边界推进过程中将所有分离的分片或分片中的缝隙缝合在特征区域.这种两阶段的三角化策略可有效地处理含尖锐特征或不规则采样如不充分采样的点集,无需保证拓扑完整性的复杂数学测试,如协变分析和三角形相交检测等,基于局部光滑曲面的求交,可有效地恢复采样点集丢失的特征信息.实验结果表明,采用文中算法能健壮处理不规则采样点集,并生成特征敏感的高质量网格.     

Streaming Surface Reconstruction Using Wavelets

We present a streaming method for reconstructing surfaces from large data sets generated by a laser range scanner using wavelets. Wavelets provide a localized, multiresolution representation of functions and this makes them ideal candidates for streaming surface reconstruction algorithms. We show how wavelets can be used to reconstruct the indicator function of a shape from a cloud of points with associated normals. Our method proceeds in several steps. We first compute a low‐resolution approximation of the indicator function using an octree followed by a second pass that incrementally adds fine resolution details. The indicator function is then smoothed using a modified octree convolution step and contoured to produce the final surface. Due to the local, multiresolution nature of wavelets, our approach results in an algorithm over 10 times faster than previous methods and can process extremely large data sets in the order of several hundred million points in only an hour.     

散乱点的快速曲面重建方法

空间散乱点的曲面重建有着广泛的应用前景，是当前国际上的研究热点之一，Crust算法是一种基于计算几何中的Voronoi周期图的曲面重建算法，它算法简单，重建结果精细，但是由于计算量太大，其应用受到了限制，为此提出了一种依据采样点的局部特征尺度对原始采样集进行不均匀降采样的方法，在保证采样集能够满足重建要求的前提下，使参与重建的表面点数大为降低，减少了重建算法的计算量，从而提高了重建的速度，这一方法还可以应用于网络简化，通过剔除某些顶点达到简化之目的。     

带洞点云多层同步表面重建方法

针对法向信息缺失和采样点缺失的带有洞的散乱点云数据,提出了一种高效高质量的多层同步表面重建方法.首先利用动态等高线检测出含有洞的八叉树节点,并且基于HPR（hidden point removal）映射计算出八叉树顶点的内外状态,建立带有顶点内外标识的空间有向状态八叉树,然后基于八叉树节点内法向测试方法保证基于k近邻表面重建过程中采样点的法向的正确性,且该空间有向状态八叉树可以支持不同层次的点云同步重建,在保证重建结果正确性的前提下,提高重建效率.