Hongwei Lin

A robust hole-filling algorithm for triangular mesh

This paper presents a novel hole-filling algorithm that can fill arbitrary holes in triangular mesh models. First, the advancing front mesh technique is used to cover the hole with newly created triangles. Next, the desirable normals of the new triangles are approximated using our desirable normal computing schemes. Finally, the three coordinates of every new vertex are re-positioned by solving the Poisson equation based on the desirable normals and the boundary vertices of the hole. Many experimental results and error evaluations are given to show the robustness and efficiency of the algorithm.

Watertight trimmed NURBS

This paper addresses the long-standing problem of the unavoidable gaps that arise when expressing the intersection of two NURBS surfaces using conventional trimmed-NURBS representation. The solution converts each trimmed NURBS into an untrimmed T-Spline, and then merges the untrimmed T-Splines into a single, watertight model. The solution enables watertight fillets of NURBS models, as well as arbitrary feature curves that do not have to follow iso-parameter curves. The resulting T-Spline representation can be exported without error as a collection of NURBS surfaces.

A Robust Hole-Filling Algorithm for Triangular Mesh

Summary form only given. This paper presents a novel hole-filling algorithm that can fill arbitrary holes in triangular mesh models. First, the advancing front mesh technique is used to cover the hole with newly created triangles. Next, the desirable normals of the new triangles are approximated using our desirable normal computing schemes. Finally, the three coordinates of every new vertex are re-positioned by solving the Poisson equation based on the desirable normals and the boundary vertices of the hole. Many experimental results and error evaluations are given to show the robustness and efficiency of the algorithm.

A mesh reconstruction algorithm driven by an intrinsic property of a point cloud

This paper presents an algorithm for reconstructing a triangle mesh surface from a given point cloud. Starting with a seed triangle, the algorithm grows a partially reconstructed triangle mesh by selecting a new point based on an intrinsic property of the point cloud, namely, the sampling uniformity degree. The reconstructed mesh is essentially an approximate minimum-weight triangulation to the point cloud constrained to be on a two-dimensional manifold. Thus, the reconstructed surface has only small topological difference from the surface of the sampled object. Topological correct reconstruction can be guaranteed by adding a post-processing step. q 2003 Elsevier Ltd. All rights reserved.

Feature suppression based CAD mesh model simplification

Dynamic simulation and high quality FEA mesh generation need the CAD mesh model to be simplified, that is, suppressing the detailed features on the mesh without any changes to the rest. However, the traditional mesh simplification methods for graphical models can not satisfy the requirements of CAD mesh simplification. In this paper, we develop a feature suppression based CAD mesh model simplification framework. First, the CAD mesh model is segmented by an improved watershed segmentation algorithm, constructing the region-level representation required by feature recognition. Second, the form features needing to be suppressed are extracted using a feature recognition method with user defined feature facility based on the region-level representation, establishing the feature-level representation. Third, every recognized feature is suppressed using the most suitable one of the three methods, i.e. planar Delaunay triangulation, Poisson equation based method, and the method for blend features, thus simplifying the CAD mesh model. Our method provides an effective way to make CAD mesh model simplification meet the requirements of engineering applications. Several experimental results are presented to show the superiority and effectivity of our approach.

Progressive and iterative approximation for least squares B-spline curve and surface fitting

The progressive and iterative approximation (PIA) method is an efficient and intuitive method for data fitting. However, in the classical PIA method, the number of the control points is equal to that of the data points. It is not feasible when the number of data points is very large. In this paper, we develop a new progressive and iterative approximation for least square fitting (LSPIA). LSPIA constructs a series of fitting curves (surfaces) by adjusting the control points iteratively, and the limit curve (surface) is the least square fitting result to the given data points. In each iteration, the difference vector for each control point is a weighted sum of some difference vectors between the data points and their corresponding points on the fitting curve (surface). Moreover, we present a simple method to compute the practical weight whose corresponding convergence rate is comparable to that of the theoretical best weight. The advantages of LSPIA are two-fold. First, with LSPIA, a very large data set can be fitted efficiently and robustly. Second, in the incremental data fitting procedure with LSPIA, a new round of iterations can be started from the fitting result of the last round of iterations, thus saving great amount of computation. Lots of empirical examples illustrated in this paper show the efficiency and effectiveness of LSPIA.

B-spline surface fitting by iterative geometric interpolation/approximation algorithms

Recently, the use of B-spline curves/surfaces to fit point clouds by iteratively repositioning the B-splines control points on the basis of geometrical rules has gained in popularity because of its simplicity, scalability, and generality. We distinguish between two types of fitting, interpolation and approximation. Interpolation generates a B-spline surface that passes through the data points, whereas approximation generates a B-spline surface that passes near the data points, minimizing the deviation of the surface from the data points. For surface interpolation, the data points are assumed to be in grids, whereas for surface approximation the data points are assumed to be randomly distributed. In this paper, an iterative geometric interpolation method, as well as an approximation method, which is based on the framework of the iterative geometric interpolation algorithm, is discussed. These two iterative methods are compared with standard fitting methods using some complex examples, and the advantages and shortcomings of our algorithms are discussed. Furthermore, we introduce two methods to accelerate the iterative geometric interpolation algorithm, as well as a method to impose geometric constraints, such as reflectional symmetry, on the iterative geometric interpolation process, and a novel fairing method for non-uniform complex data points. Complex examples are provided to demonstrate the effectiveness of the proposed algorithms.

Local progressive-iterative approximation format for blending curves and patches

Just by adjusting the control points iteratively, progressive-iterative approximation presents an intuitive and straightforward way to fit data points. It generates a curve or patch sequence with finer and finer precision, and the limit of the sequence interpolates the data points. The progressive-iterative approximation brings more flexibility for shape controlling in data fitting. In this paper, we design a local progressive-iterative approximation format, and show that the local format is convergent for the blending curve with normalized totally positive basis, and the bi-cubic B-spline patch, which is the most commonly used patch in geometric design. Moreover, a special adjustment manner is designed to make the local progressive-iterative approximation format is convergent for a generic blending patch with normalized totally positive basis. The local progressive-iterative approximation format adjusts only a part of the control points of a blending curve or patch, and the limit curve or patch interpolates the corresponding data points. Based on the local format, data points can be fit adaptively.

Short Communication to SMI 2011: CAD mesh model segmentation by clustering

CAD mesh models have been widely employed in current CAD/CAM systems, where it is quite useful to recognize the features of the CAD mesh models. The first step of feature recognition is to segment the CAD mesh model into meaningful parts. Although there are lots of mesh segmentation methods in literature, the majority of them are not suitable to CAD mesh models. In this paper, we design a mesh segmentation method based on clustering, dedicated to the CAD mesh model. Specifically, by the agglomerative clustering method, the given CAD mesh model is first clustered into the sparse and dense triangle regions. Furthermore, the sparse triangle region is separated into planar regions, cylindrical regions, and conical regions by the Gauss map of the triangular faces and Hough transformation; the dense triangle region is also segmented by the mean shift operation performed on the mean curvature field defined on the mesh faces. Lots of empirical results demonstrate the effectiveness and efficiency of the CAD mesh segmentation method in this paper.

Adaptive patch-based mesh fitting for reverse engineering

In this paper, we propose a novel adaptive mesh fitting algorithm that fits a triangular model with G^1 smoothly stitching bi-quintic Bezier patches. Our algorithm first segments the input mesh into a set of quadrilateral patches, whose boundaries form a quadrangle mesh. For each boundary of each quadrilateral patch, we construct a normal curve and a boundary-fitting curve, which fit the normal and position of its boundary vertices respectively. By interpolating the normal and boundary-fitting curves of each quadrilateral patch with a Bezier patch, an initial G^1 smoothly stitching Bezier patches is generated. We perform this patch-based fitting scheme in an adaptive fashion by recursively subdividing the underlying quadrilateral into four sub-patches. The experimental results show that our algorithm achieves precision-ensured Bezier patches with G^1 continuity and meets the requirements of reverse engineering.