A framework for hull form reverse engineering and geometry integration into numerical simulations

The thesis presents a ship hull form specific reverse engineering and CAD integration framework. The reverse engineering part proposes three alternative suitable reconstruction approaches namely curves network, direct surface fitting, and triangulated surface reconstruction. The CAD integration part includes surface healing, region identification, and domain preparation strategies which used to adapt the CAD model to downstream application requirements. In general, the developed framework bridges a point cloud and a CAD model obtained from IGES and STL file into downstream applications

A Survey of Surface Reconstruction from Point Clouds

International audienceThe area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contains a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations – not necessarily the explicit geometry. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections, and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques, and provide directions for future work in surface reconstruction

Robust and parallel mesh reconstruction from unoriented noisy points.

Sheung, Hoi.Thesis (M.Phil.)--Chinese University of Hong Kong, 2009.Includes bibliographical references (p. 65-70).Abstract also in Chinese.Abstract --- p.vAcknowledgements --- p.ixList of Figures --- p.xiiiList of Tables --- p.xvChapter 1 --- Introduction --- p.1Chapter 1.1 --- Main Contributions --- p.3Chapter 1.2 --- Outline --- p.3Chapter 2 --- Related Work --- p.5Chapter 2.1 --- Volumetric reconstruction --- p.5Chapter 2.2 --- Combinatorial approaches --- p.6Chapter 2.3 --- Robust statistics in surface reconstruction --- p.6Chapter 2.4 --- Down-sampling of massive points --- p.7Chapter 2.5 --- Streaming and parallel computing --- p.7Chapter 3 --- Robust Normal Estimation and Point Projection --- p.9Chapter 3.1 --- Robust Estimator --- p.9Chapter 3.2 --- Mean Shift Method --- p.11Chapter 3.3 --- Normal Estimation and Projection --- p.11Chapter 3.4 --- Moving Least Squares Surfaces --- p.14Chapter 3.4.1 --- Step 1: local reference domain --- p.14Chapter 3.4.2 --- Step 2: local bivariate polynomial --- p.14Chapter 3.4.3 --- Simpler Implementation --- p.15Chapter 3.5 --- Robust Moving Least Squares by Forward Search --- p.16Chapter 3.6 --- Comparison with RMLS --- p.17Chapter 3.7 --- K-Nearest Neighborhoods --- p.18Chapter 3.7.1 --- Octree --- p.18Chapter 3.7.2 --- Kd-Tree --- p.19Chapter 3.7.3 --- Other Techniques --- p.19Chapter 3.8 --- Principal Component Analysis --- p.19Chapter 3.9 --- Polynomial Fitting --- p.21Chapter 3.10 --- Highly Parallel Implementation --- p.22Chapter 4 --- Error Controlled Subsampling --- p.23Chapter 4.1 --- Centroidal Voronoi Diagram --- p.23Chapter 4.2 --- Energy Function --- p.24Chapter 4.2.1 --- Distance Energy --- p.24Chapter 4.2.2 --- Shape Prior Energy --- p.24Chapter 4.2.3 --- Global Energy --- p.25Chapter 4.3 --- Lloyd´ةs Algorithm --- p.26Chapter 4.4 --- Clustering Optimization and Subsampling --- p.27Chapter 5 --- Mesh Generation --- p.29Chapter 5.1 --- Tight Cocone Triangulation --- p.29Chapter 5.2 --- Clustering Based Local Triangulation --- p.30Chapter 5.2.1 --- Initial Surface Reconstruction --- p.30Chapter 5.2.2 --- Cleaning Process --- p.32Chapter 5.2.3 --- Comparisons --- p.33Chapter 5.3 --- Computing Dual Graph --- p.34Chapter 6 --- Results and Discussion --- p.37Chapter 6.1 --- Results of Mesh Reconstruction form Noisy Point Cloud --- p.37Chapter 6.2 --- Results of Clustering Based Local Triangulation --- p.47Chapter 7 --- Conclusions --- p.55Chapter 7.1 --- Key Contributions --- p.55Chapter 7.2 --- Factors Affecting Our Algorithm --- p.55Chapter 7.3 --- Future Work --- p.56Chapter A --- Building Neighborhood Table --- p.59Chapter A.l --- Building Neighborhood Table in Streaming --- p.59Chapter B --- Publications --- p.63Bibliography --- p.6

What's the Situation with Intelligent Mesh Generation: A Survey and Perspectives

Intelligent Mesh Generation (IMG) represents a novel and promising field of research, utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG has significantly broadened the adaptability and practicality of mesh generation techniques, delivering numerous breakthroughs and unveiling potential future pathways. However, a noticeable void exists in the contemporary literature concerning comprehensive surveys of IMG methods. This paper endeavors to fill this gap by providing a systematic and thorough survey of the current IMG landscape. With a focus on 113 preliminary IMG methods, we undertake a meticulous analysis from various angles, encompassing core algorithm techniques and their application scope, agent learning objectives, data types, targeted challenges, as well as advantages and limitations. We have curated and categorized the literature, proposing three unique taxonomies based on key techniques, output mesh unit elements, and relevant input data types. This paper also underscores several promising future research directions and challenges in IMG. To augment reader accessibility, a dedicated IMG project page is available at \url{https://github.com/xzb030/IMG_Survey}

Doctor of Philosophy

dissertationShape analysis is a well-established tool for processing surfaces. It is often a first step in performing tasks such as segmentation, symmetry detection, and finding correspondences between shapes. Shape analysis is traditionally employed on well-sampled surfaces where the geometry and topology is precisely known. When the form of the surface is that of a point cloud containing nonuniform sampling, noise, and incomplete measurements, traditional shape analysis methods perform poorly. Although one may first perform reconstruction on such a point cloud prior to performing shape analysis, if the geometry and topology is far from the true surface, then this can have an adverse impact on the subsequent analysis. Furthermore, for triangulated surfaces containing noise, thin sheets, and poorly shaped triangles, existing shape analysis methods can be highly unstable. This thesis explores methods of shape analysis applied directly to such defect-laden shapes. We first study the problem of surface reconstruction, in order to obtain a better understanding of the types of point clouds for which reconstruction methods contain difficulties. To this end, we have devised a benchmark for surface reconstruction, establishing a standard for measuring error in reconstruction. We then develop a new method for consistently orienting normals of such challenging point clouds by using a collection of harmonic functions, intrinsically defined on the point cloud. Next, we develop a new shape analysis tool which is tolerant to imperfections, by constructing distances directly on the point cloud defined as the likelihood of two points belonging to a mutually common medial ball, and apply this for segmentation and reconstruction. We extend this distance measure to define a diffusion process on the point cloud, tolerant to missing data, which is used for the purposes of matching incomplete shapes undergoing a nonrigid deformation. Lastly, we have developed an intrinsic method for multiresolution remeshing of a poor-quality triangulated surface via spectral bisection