三维散乱点云快速曲面重建算法

提出了一种基于Delaunay三角剖分的三维散乱点云快速曲面重建算法。算法首先计算点云的Delaunay三角剖分, 从Delaunay四面体提取初始三角网格, 根据Voronoi体元的特征构造优先队列并生成种子三角网格, 然后通过区域生长的方式进行流形提取。实验结果表明, 该算法可以高效、稳定地重构具有复杂拓扑结构、非封闭曲面甚至是非均匀采样的点云数据。与传统的基于Delaunay的方法比较, 该算法仅需要进行一次Delaunay三角剖分, 无须极点的计算, 因此算法的重构速度快。     

Delaunay三角剖分算法改进与对比分析

针对Delaunay算法的计算速度问题,从数据结构和算法两个方面加以改进。对Delaunay三角剖分的代数拓扑分析,设计一种顺序存贮的Hash数据结构,实现临时单纯形对象的快速和顺序存取、查询、插入和删除等操作;以单纯形边对象的活性分析为核心,以Hash数据结构进行操作,消去生长法的递归过程;此外,提出基于微切平面的生长法,将基于空间四面体的空球搜索降维至局部二维的空圆搜索。对汽车挡泥板和兔子模型进行三角剖分实验,实验结果表明,消去递归的生长法和基于微切平面的生长法和传统的生长法三角剖分效果相同,但是计算速度比传统方法效率更高。     

一个利用法矢的散乱点三角剖分算法

曲面上散乱点的三角剖分在曲面重建中发挥着重要作用,借助于曲面上的法矢信息和三维Delaunay三角剖分算法,该文给出了一种新的散乱点三角剖分算法,输入一组散乱点以及所在曲面S在这些散乱点处的一致定向的法矢信息,该算法将产生一张插值散乱点的三角网格曲面M,并且曲面M可以近似地看成是曲面S的三角剖分,算法的主要步骤分为两步：首先通过曲面S的一致定向的法矢信息,在曲面S的同一侧添加辅助点,利用这些辅助点来剔除Delaunay三角剖分中产生的不需要的三角片;然后将剩余的三角片连接成一张完整的网格曲面,与基于中轴的三角剖分算法相比,该文算法需要更少和更简单的计算,与局部三角剖分算法相比,该文算法可以更有效地避免重建后的曲面产生自交,该文的算法可用于任意拓扑的光滑曲面重建。     

一种基于边指针搜索及区域划分的三角剖分算法

针对大规模数据处理时Delaunay三角剖分过于耗时的问题, 本文提出了一种基于边指针搜索及区域划分的三角剖分算法.基于边指针设计了一种能够反映三角形之间位置关系的数据结构, 并优化了目标三角形的搜索路径.基于该数据结构, 利用区域划分进一步降低目标三角形的搜索深度.超级三角形所在的正方形被划分成具有相同尺寸的区域, 目标三角形的搜索从插入点所在的区域的入口三角形开始, 这大大缩小了目标三角形的搜索范围.实验证明, 与传统的Delaunay三角剖分算法相比, 该算法的效率显著提升.     

利用Voronoi图改进离散数据重构曲面算法

本文利用Delaunay三角剖分和 Voronoi图的性质,实现了一种对散乱点重构闭合曲面的方法。该方法在搜索策略上进行了改进：首先对输入点进行三角剖分,产生相互独立的四面体,构建一个凸包;然后利用Delaunay三角剖分产生Voronoi图;最后根据Voronoi图的性质,选择包含在形体内部的四面体,提取出边界三角形,完成散乱点边界重构。计算复杂度和Delaunay四面体数量成正比,在自动形状重构时形状边界提取过程的计算复杂度为O(n),算法适用于各种涉及图形重构的工程应用。     

一种基于映射法的散乱点云Delaunay三角剖分算法

针对直接在三维空间构建海量点云的Delaunay三角网格效率低下,提出一种新的基于映射法的Delau-nay三角网格构建算法.首先提出一种基于区域增长法的点云分片方法,能够保证对分片后的点云数据进行映射而不产生重叠;然后保持空间点云之间的距离特性,将三维点云映射到二维平面;在二维平面内进行Delaunay三角剖分,再将结果返回到三维空间内.实验结果表明,算法能够构建质量较好的三角网格.由于该算法将点云的三角剖分转换到低维空间,通过实验结果对比本算法与其他算法效果,证明该方法能够更快地完成重构.     

基于泊松方程的孔洞修补算法

传统网格生长法对孔洞数量庞大且孔洞类型复杂的三维网格模型修复效果不佳。针对该问题,将泊松方程应用于三角网格模型的孔洞修补。利用原始模型信息建立泊松方程,对输入模型曲面进行全局拟合,根据孔洞信息裁剪拟合得到的预测曲面并与原始孔洞模型缝合,通过孔洞边界区域法向量信息调整修补曲面的三角面片方向,达到特征增强的目的。实验结果表明,该算法对于结构复杂的多孔洞三维模型修补效果较好,对噪声鲁棒性强,在保留模型原始信息的同时能够准确还原孔洞区域特征。     

一种基于剖分与聚类的三维手势建模方法

针对有些建模方法需要设置复杂参数,或所建模型不够平滑、完整,以及出现多余网格等问题,提出了一种基于Delaunay三角剖分与K-Means聚类的三维手势静态建模方法。首先对点云进行半径滤波,再把点云投影在X0Y平面上,然后进行Delaunay三角剖分,对于剖分得到的三角网格中的长边与短边,使用K-Means算法聚为两类,最后删除含有长边的三角面片,得到完整手势的三维模型。实验结果表明,该方法有利于减少多余的网格面,形成的三维手势模型的网格面也较为平滑、完整。     

一种基于三维Delaunay三角化的曲面重建算法

提出一种基于三维Delaunay三角化的区域增长式曲面重建方法。该方法以空间点云的Delaunay三角化为基础,结合局部区域增长的曲面构造,较以往方法具有人为参与更少、适用范围更广的优点。算法采用增量式插入点的方式构建空间Delaunay划分,采用广度优先算法,以外接圆最小为准则从Delaunay三角化得到的四面体中抽取出合适的三角片构成曲面。该算法的设计无须计算原始点集的法矢,且孔洞系数对重建的结果影响很小,重建出的三角网格面更符合原始曲面的几何特征。无论待建曲面是否是封闭曲面,本算法均可获得较好的重建效果。     

一种基于点云数据的快速曲面重构方法

研究激光扫描中的点云数据重构技术,提出一种基于规则点云数据的快速曲面重构方法。分析相邻扫描线之间数据点的相对位置关系,在三角剖分的基础上,设计改进的扫描线剖分算法,根据激光逐行扫描的特点,对点云数据进行不规则三角网格划分,利用几何关系进行配对构网,并在所建三角模型的基础上实现三角网格的局部优化和纹理映射,得到重建模型。实验结果表明,与传统Delaunay空间三角剖分算法相比,该算法可明显提高三角构网速度和质量,消除空洞,改善重建效果。     

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.     

Sketching freeform meshes using graph rotation functions

We present a new approach for sketching free form meshes with topology consistency. Firstly, we interpret the given 2D curve to be the projection of the 3D curve with the minimum curvature. Then we adopt a topology-consistent strategy based on the graph rotation system, to trace the simple faces on the interconnecting 3D curves. With the face tracing algorithm, our system can identify the 3D surfaces automatically. After obtaining the boundary curves for the faces, we apply Delaunay triangulation on these faces. Finally, the shape of the triangle mesh that follows the 3D boundary curves is computed by using harmonic interpolation. Meanwhile our system provides real-time algorithms for both control curve generation and the subsequent surface optimization. With the incorporation of topological manipulation into geometrical modeling, we show that automatically generated models are both beneficial and feasible.     

基于曲面局平特性的散乱数据拓扑重建算法

提出了一种基于曲面局平特性的,以散乱点集及其密度指标作为输入,以三角形分片线性曲面作为输出的拓扑重建算法.算法利用曲面的局平特性,从散乱点集三维Delaunay三角剖分的邻域结构中完成每个样点周围的局部拓扑重建,并从局部重建的并集中删除不相容的三角形,最终得到一个二维流形拓扑曲面集作为重建结果.该算法适应于包括单侧曲面在内的任意不自交的拓扑曲面集,并且重建结果是相对优化的曲面三角形剖分,可以应用于科学计算可视化、雕塑曲面造型和反求工程等领域.     

Implicit Surface-Based Geometric Fusion

This paper introduces a general purpose algorithm for reliable integration of sets of surface measurements into a single 3D model. The new algorithm constructs a single continuous implicit surface representation which is the zero-set of a scalar field function. An explicit object model is obtained using any implicit surface polygonization algorithm. Object models are reconstructed from both multiple view conventional 2.5D range images and hand-held sensor range data. To our knowledge this is the first geometric fusion algorithm capable of reconstructing 3D object models from noisy hand-held sensor range data.This approach has several important advantages over existing techniques. The implicit surface representation allows reconstruction of unknown objects of arbitrary topology and geometry. A continuous implicit surface representation enables reliable reconstruction of complex geometry. Correct integration of overlapping surface measurements in the presence of noise is achieved using geometric constraints based on measurement uncertainty. The use of measurement uncertainty ensures that the algorithm is robust to significant levels of measurement noise. Previous implicit surface-based approaches use discrete representations resulting in unreliable reconstruction for regions of high curvature or thin surface sections. Direct representation of the implicit surface boundary ensures correct reconstruction of arbitrary topology object surfaces. Fusion of overlapping measurements is performed using operations in 3D space only. This avoids the local 2D projection required for many previous methods which results in limitations on the object surface geometry that is reliably reconstructed. All previous geometric fusion algorithms developed for conventional range sensor data are based on the 2.5D image structure preventing their use for hand-held sensor data. Performance evaluation of the new integration algorithm against existing techniques demonstrates improved reconstruction of complex geometry.     

3D Topology Preserving Flows for Viewpoint-Based Cortical Unfolding

We present a variational method for unfolding of the cortex based on a user-chosen point of view as an alternative to more traditional global flattening methods, which incur more distortion around the region of interest. Our approach involves three novel contributions. The first is an energy function and its corresponding gradient flow to measure the average visibility of a region of interest of a surface with respect to a given viewpoint. The second is an additional energy function and flow designed to preserve the 3D topology of the evolving surface. The third is a method that dramatically improves the computational speed of the 3D topology preservation approach by creating a tree structure of the 3D surface and using a recursion technique. Experiments results show that the proposed approach can successfully unfold highly convoluted surfaces such as the cortex while preserving their topology during the evolution.     

基于标准差的地形三维表面模型建立方法*

针对二维数据域的Delaunay三角剖分推广到空间时存在的关键问题,提出一种基于标准差的地形三维表面模型建立算法.与空外接圆准则相比,该算法在计算时考虑了附加高程信息,引入标准差作为构网判断准则.剖析了标准差的含义,为什么使用标准差以及基于标准差的剖分准则,最后给出基于标准差的地形三维表面模型建立算法的具体步骤,并通过一个具体实例对算法进行了验证.     

An algorithm for calculating the similarity measures of surfaces represented as point clouds

The problem of estimating the similarity of surfaces represented as point clouds is studied. An approach is suggested to the comparison of such surfaces based on the surface approximation by piecewise-linear functions of two variables in the Delaunay triangulations. The approach is based on the developed algorithm for comparison of the functions of two variables specified on various irregular sets of points. Possible applications of this algorithm were considered such as the problems of 3D portraits comparison, e.g., problems of estimation of facial asymmetry and dynamics of lower jaw movements on the basis of a 3D video sequence of models. The performed experiments confirmed the correctness, validity, and computational efficiency of the proposed algorithm.