A polygon mesh, or a 3D mesh, consisting of a collection of vertices, edges, and polygons in three-dimensional space, is the standard way of representing 3D objects. In practice, polygon meshes acquired from the 3D scanning process fail to meet the quality requirements for most practical applications. Mesh defects like holes, duplicate elements, non-manifold elements are introduced during the scanning process, which lowers the quality of the output meshes. In this thesis, we describe a complete mesh-repairing process that fixes all defects within a polygon mesh. This process is divided into two parts: the mesh-cleaning part and the hole-filling part. In the mesh-cleaning part, we describe the ways of repairing different types of mesh defects. In the hole-filling part, we discuss two main hole-filling approaches: the surface-based method and the volumetric. In addition, we present a hybrid algorithm by combining the surface-based approach and the volumetric approach. We compare the meshes created by different hole-filing algorithms and show that the new algorithm is a good alternative to the existing ones. / Thesis (Master, Computing) -- Queen's University, 2013-09-11 23:45:08.591

A novel progressive lossy-to-lossless coding method is proposed for mesh models of images whose underlying triangulations have arbitrary connectivity. For a triangulation T of a set P of points, our proposed method represents the connectivity of T as a sequence of edge flips that maps a uniquely-determined Delaunay triangulation (i.e., preferred-directions Delaunay triangulation) of P to T. The coding efficiency of our method is highest when the underlying triangulation connectivity is close to Delaunay, and slowly degrades as connectivity moves away from being Delaunay. Through experimental results, we show that our proposed coding method is able to significantly outperform a simple baseline coding scheme. Furthermore, our proposed method can outperform traditional connectivity coding methods for meshes that do not deviate too far from Delaunay connectivity. This result is of practical significance since, in many applications, mesh connectivity is often not so far from being Delaunay, due to the good approximation properties of Delaunay triangulations. / Graduate

Two highly effective content-adaptive methods for generating Delaunay mesh models of images, known as IID1 and IID2, are proposed. The methods repeatedly alternate between mesh simplification and refinement, based on the incremental/decremental mesh-generation framework of Adams, which has several free parameters. The effect of different choices of the framework's free parameters is studied, and the results are used to derive two mesh-generation methods that differ in computational complexity. The higher complexity IID2 method generates mesh models of superior reconstruction quality, while the lower complexity IID1 method trades mesh quality in return for a decrease in computational cost. Some of the contributions of our work include the recommendation of a better choice for the growth-schedule parameter of the framework, as well as the use of Floyd-Steinberg error diffusion for the initial-mesh selection. As part of our work, we evaluated the performance of the proposed methods using a data set of 50 images varying in type (e.g., photographic, computer generated, and medical), size and bit depth with multiple target mesh densities ranging from 0.125% to 4%. The experimental results show that our proposed methods perform extremely well, yielding high-quality image approximations in terms of peak-signal-to-noise ratio (PSNR) and subjective visual quality, at an equivalent or lower computational cost compared to other well known approaches such as the ID1, ID2, and IDDT methods of Adams, and the greedy point removal (GPR) scheme of Demaret and Iske. More specifically, the IID2 method outperforms the GPR scheme in terms of mesh quality by 0.2-1.0 dB with a 62-93% decrease in computational cost. Furthermore, the IID2 method yields meshes of similar quality to the ID2 method at a computational cost that is lower by 9-41%. The IID1 method provides improvements in mesh quality in 93% of the test cases by margins of 0.04-1.31 dB compared to the IDDT scheme, while having a similar complexity. Moreover, reductions in execution time of 4-59% are achieved compared to the ID1 method in 86% of the test cases. / Graduate / 0544, 0984, 0537 / marzouki@uvic.ca

Triangle-mesh modeling, as one of the approaches for representing images based on nonuniform sampling, has become quite popular and beneficial in many applications. In this thesis, image representation using triangle-mesh models and its application in image scaling are studied. Consequently, two new methods, namely, the SEMMG and MIS methods are proposed, where each solves a different problem. In particular, the SEMMG method is proposed to address the problem of image representation by producing effective mesh models that are used for representing grayscale images, by minimizing squared error. The MIS method is proposed to address the image-scaling problem for grayscale images that are approximately piecewise-smooth, using triangle-mesh models. The SEMMG method, which is proposed for addressing the mesh-generation problem, is developed based on an earlier work, which uses a greedy-point-insertion (GPI) approach to generate a mesh model with explicit representation of discontinuities (ERD). After in-depth analyses of two existing methods for generating the ERD models, several weaknesses are identified and specifically addressed to improve the quality of the generated models, leading to the proposal of the SEMMG method. The performance of the SEMMG method is then evaluated by comparing the quality of the meshes it produces with those obtained by eight other competing methods, namely, the error-diffusion (ED) method of Yang, the modified Garland-Heckbert (MGH) method, the ERDED and ERDGPI methods of Tu and Adams, the Garcia-Vintimilla-Sappa (GVS) method, the hybrid wavelet triangulation (HWT) method of Phichet, the binary space partition (BSP) method of Sarkis, and the adaptive triangular meshes (ATM) method of Liu. For this evaluation, the error between the original and reconstructed images, obtained from each method under comparison, is measured in terms of the PSNR. Moreover, in the case of the competing methods whose implementations are available, the subjective quality is compared in addition to the PSNR. Evaluation results show that the reconstructed images obtained from the SEMMG method are better than those obtained by the competing methods in terms of both PSNR and subjective quality. More specifically, in the case of the methods with implementations, the results collected from 350 test cases show that the SEMMG method outperforms the ED, MGH, ERDED, and ERDGPI schemes in approximately 100%, 89%, 99%, and 85% of cases, respectively. Moreover, in the case of the methods without implementations, we show that the PSNR of the reconstructed images produced by the SEMMG method are on average 3.85, 0.75, 2, and 1.10 dB higher than those obtained by the GVS, HWT, BSP, and ATM methods, respectively. Furthermore, for a given PSNR, the SEMMG method is shown to produce much smaller meshes compared to those obtained by the GVS and BSP methods, with approximately 65% to 80% fewer vertices and 10% to 60% fewer triangles, respectively. Therefore, the SEMMG method is shown to be capable of producing triangular meshes of higher quality and smaller sizes (i.e., number of vertices or triangles) which can be effectively used for image representation. Besides the superior image approximations achieved with the SEMMG method, this work also makes contributions by addressing the problem of image scaling. For this purpose, the application of triangle-mesh mesh models in image scaling is studied. Some of the mesh-based image-scaling approaches proposed to date employ mesh models that are associated with an approximating function that is continuous everywhere, which inevitably yields edge blurring in the process of image scaling. Moreover, other mesh-based image-scaling approaches that employ approximating functions with discontinuities are often based on mesh simplification where the method starts with an extremely large initial mesh, leading to a very slow mesh generation with high memory cost. In this thesis, however, we propose a new mesh-based image-scaling (MIS) method which firstly employs an approximating function with selected discontinuities to better maintain the sharpness at the edges. Secondly, unlike most of the other discontinuity-preserving mesh-based methods, the proposed MIS method is not based on mesh simplification. Instead, our MIS method employs a mesh-refinement scheme, where it starts from a very simple mesh and iteratively refines the mesh to reach a desirable size. For developing the MIS method, the performance of our SEMMG method, which is proposed for image representation, is examined in the application of image scaling. Although the SEMMG method is not designed for solving the problem of image scaling, examining its performance in this application helps to better understand potential shortcomings of using a mesh generator in image scaling. Through this examination, several shortcomings are found and different techniques are devised to address them. By applying these techniques, a new effective mesh-generation method called MISMG is developed that can be used for image scaling. The MISMG method is then combined with a scaling transformation and a subdivision-based model-rasterization algorithm, yielding the proposed MIS method for scaling grayscale images that are approximately piecewise-smooth. The performance of our MIS method is then evaluated by comparing the quality of the scaled images it produces with those obtained from five well-known raster-based methods, namely, bilinear interpolation, bicubic interpolation of Keys, the directional cubic convolution interpolation (DCCI) method of Zhou et al., the new edge-directed image interpolation (NEDI) method of Li and Orchard, and the recent method of super-resolution using convolutional neural networks (SRCNN) by Dong et al.. Since our main goal is to produce scaled images of higher subjective quality with the least amount of edge blurring, the quality of the scaled images are first compared through a subjective evaluation followed by some objective evaluations. The results of the subjective evaluation show that the proposed MIS method was ranked best overall in almost 67\% of the cases, with the best average rank of 2 out of 6, among 380 collected rankings with 20 images and 19 participants. Moreover, visual inspections on the scaled images obtained with different methods show that the proposed MIS method produces scaled images of better quality with more accurate and sharper edges. Furthermore, in the case of the mesh-based image-scaling methods, where no implementation is available, the MIS method is conceptually compared, using theoretical analysis, to two mesh-based methods, namely, the subdivision-based image-representation (SBIR) method of Liao et al. and the curvilinear feature driven image-representation (CFDIR) method of Zhou et al.. / Graduate

The goal of this thesis is to use machine learning methods for datasets of scanned faces and to create a program that allows to explore and edit faces represented as triangle meshes with a number of controls. Firstly we had to reduce dimension of triangle meshes by PCA and then we tried to predict shape of meshes according to physical properties like weight, height, age and BMI. The modeled faces can be used in animation or games.

Many digital models used in entertainment, medical visualization, material science, architecture, Geographic Information Systems (GIS), and mechanical Computer Aided Design (CAD) are defined in terms of their boundaries. These boundaries are often approximated using triangle meshes. The complexity of models, which can be measured by triangle count, increases rapidly with the precision of scanning technologies and with the need for higher resolution. An increase in mesh complexity results in an increase of storage requirement, which in turn increases the frequency of disk access or cache misses during mesh processing, and hence decreases performance. For example, in a test application involving a mesh with 55 million triangles in a machine with 4GB of memory versus a machine with 1GB of memory, performance decreases by a factor of about 6000 because of memory thrashing. To help reduce memory thrashing, we focus on decreasing the average storage requirement per triangle measured in 32-bit integer references per triangle (rpt). This thesis covers compact connectivity representation for triangle meshes and discusses four data structures: 1. Sorted Opposite Table (SOT), which uses 3 rpt and has been extended to support tetrahedral meshes. 2. Sorted Quad (SQuad), which uses about 2 rpt and has been extended to support streaming. 3. Laced Ring (LR), which uses about 1 rpt and offers an excellent compromise between storage compactness and performance of mesh traversal operators. 4. Zipper, an extension of LR, which uses about 6 bits per triangle (equivalently 0.19 rpt), therefore is the most compact representation. The triangle mesh data structures proposed in this thesis support the standard set of mesh connectivity operators introduced by the previously proposed Corner Table at an amortized constant time complexity. They can be constructed in linear time and space from the Corner Table or any equivalent representation. If geometry is stored as 16-bit coordinates, using Zipper instead of the Corner Table increases the size of the mesh that can be stored in core memory by a factor of about 8.

Accurate computational representations of highly deformable surfaces are indispensable in the fields of computer animation, medical simulation, computer vision, digital modeling, and computational physics. The focus of this dissertation is on the animation of physics-based phenomena with highly detailed deformable surfaces represented by triangle meshes. We first present results from an algorithm that generates continuum mechanics animations with intricate surface features. This method combines a finite element method with a tetrahedral mesh generator and a high resolution surface mesh, and it is orders of magnitude more efficient than previous approaches. Next, we present an efficient solution for the challenging problem of computing topological changes in detailed dynamic surface meshes. We then introduce a new physics-inspired surface tracking algorithm that is capable of preserving arbitrarily thin features and reproducing realistic fine-scale topological changes like Rayleigh-Plateau instabilities. This physics-inspired surface tracking technique also opens the door for a unique coupling between surficial finite element methods and volumetric finite difference methods, in order to simulate liquid surface tension phenomena more efficiently than any previous method. Due to its dramatic increase in computational resolution and efficiency, this method yielded the first computer simulations of a fully developed crown splash with droplet pinch off.

Data-dependent triangulation (DDT) based mesh-generation schemes for image representation are studied. A flexible mesh-generation framework and a highly effective mesh-generation method that employs this framework are proposed. The proposed framework is derived from frameworks proposed by Rippa and Garland and Heckbert by making a number of key modifications to facilitate the development of much more effective mesh-generation methods. As the proposed framework has several free parameters, the effects of different choices of these parameters on mesh quality (both in terms of squared error and subjectively) are studied, leading to the recommendation of a particular set of choices for these parameters. A new mesh-generation method is then introduced that employs the proposed framework with these best parameter choices. Experimental results show our proposed mesh-generation method outperforms several competing approaches, namely, the DDT-based incremental scheme proposed by Garland and Heckbert, the COMPRESS scheme proposed by Rippa, and the adaptive thinning scheme proposed by Demaret and Iske. More specifically, in terms of PSNR, our proposed method was found to outperform these three schemes by median margins of 4.1 dB, 10.76 dB, and 0.83 dB, respectively. The subjective qualities of reconstructed images were also found to be correspondingly better. In terms of computational cost, our proposed method was found to be comparable to the schemes proposed by Garland and Heckbert and Rippa. Moreover, our proposed method requires only about 5 to 10% of the time of the scheme proposed by Demaret and Iske. In terms of memory cost, our proposed method was shown to require essentially same amount of memory as the schemes proposed by Garland and Heckbert and Rippa, and orders of magnitude (33 to 800 times) less memory than the scheme proposed by Demaret and Iske. / Graduate

Geometric morphometry serves biologists and anthropologists to rigorously and quantitatively describe shapes. These representations can be treated as a statistical sample, allowing the researchers to study its variability within groups and correlate it to other features. Geometric morphometry uses landmarks as the proxy for shape, with consistent semantics in each specimen. General triangle meshes do not have this property, and as such, semantically consistent remeshes must be created artificially. This thesis deals with the design of an algorithm that consistently resamples a set of surface models for the purpose of statistical analysis. Coherent point drift was employed to perform nonrigid registration, whose result is then used to generate a semantically consistent remeshes. This approach was successfully applied in a number of studies. As CPD is compute-intensive, we propose methods of accelerating both its initialization and processing phases. Also, an extension was introduced, that can map the deviation of the surfaces from perfect bilateral symmetry and analyze it in a sample, which is significant, among others, for quantification of pathologies. Manual trimming of the surfaces and merging datasets results in outlier regions in the individual surfaces and potentially large differences in their vertex...

Despite being the prefered approach for still-image compression for nearly a decade, wavelet-based coding for video has been slow to emerge, due primarily to the fact that the shift variance of the discrete wavelet transform hinders motion estimation and compensation crucial to modern video coders. Recently it has been recognized that a redundant, or overcomplete, wavelet transform is shift invariant and thus permits motion prediction in the wavelet domain. In this dissertation, other uses for the redundancy of overcomplete wavelet transforms in video coding are explored. First, it is demonstrated that the redundant-wavelet domain facilitates the placement of an irregular triangular mesh to video images, thereby exploiting transform redundancy to implement geometries for motion estimation and compensation more general than the traditional block structure widely employed. As the second contribution of this dissertation, a new form of multihypothesis prediction, redundant wavelet multihypothesis, is presented. This new approach to motion estimation and compensation produces motion predictions that are diverse in transform phase to increase prediction accuracy. Finally, it is demonstrated that the proposed redundant-wavelet strategies complement existing advanced video-coding techniques and produce significant performance improvements in a battery of experimental results.