Generalization of tiled models with curved surfaces using typification

Especially for landmark buildings or in the context of cultural heritage documentation, highly detailed digital models are being created in many places. In some of these models, surfaces are represented by tiles which are individually modeled as solid shapes. In many applications, the high complexity of these models has to be reduced for more x efficient visualization and analysis. In our paper, we introduce an approach to derive versions at different scales from such a model through the generalization method of typification that works for curved underlying surfaces. Using the example of tiles placed on a curved roof - which occur, for example, very frequently in ancient Chinese architecture, the original set of tiles is replaced by fewer but bigger tiles while keeping a similar appearance. In the first step, the distribution of the central points of the tiles is approximated by a spline surface. This is necessary because curved roof surfaces cannot be approximated by planes at large scales. After that, the new set of tiles with less rows and/or columns is distributed along a spline surface generated from a morphing of the original surface towards a plane. The degree of morphing is dependent on the desired target scale. If the surface can be represented as a plane at the given resolution, the tiles may be converted to a bump map or a simple texture for visualization. In the final part, a perception-based method using CSF (contrast sensitivity function) is introduced to determine an appropriate LoD (level of detail) version of the model for a given viewing scenario (point of view and camera properties) at runtime.BMBF/GDI-Grid projectNational Basic Reseach Program of China/2010CB731800National High Technology Research and Development Program of China/2008AA12160

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Visualization of three-dimensional (3D) computer-aided design model is an integral part of the design process. Large assemblies such as plant or building designs contain a substantial amount of geometric data. New constraints for visualization performance and the amount of geometric data are set by the advent of mobile devices and virtual reality headsets. Our goal is to improve visualization performance and reduce memory consumption by simplifying 3D models while retaining the output simplification quality stable regardless of the geometric complexity of the input mesh. We research the current state of 3D mesh simplification methods that use geometry decimation. We design and implement our own data structure for geometry decimation. Based on the existing research, we select and use an edge decimation method for model simplification. In order to free the user from configuring edge decimation level per model by hand, and to retain a stable quality of the simplification output, we propose a threshold parameter, \textit{edge decimation cost threshold}. The threshold is calculated by multiplying the length of the model’s bounding box diagonal with a user-defined scale parameter. Our results show that the edge decimation cost threshold works as expected. The geometry decimation algorithm manages to simplify models with round surfaces with an excellent simplification rate. Based on the edge decimation cost threshold, the algorithm terminates the geometry decimation for models that have a large number of planar surfaces. Without the threshold, the simplification leads to large geometric errors quickly. The visualization performance improvement from the simplification scales almost at the same rate as the simplification rate

Metric representations for shape analysis and synthesis

2D and 3D geometric shapes are ubiquitous in computer graphics, computer animation, and computer-aided design and manufacturing. Two of the fundamental research challenges that underline these applications are the analysis and synthesis of shapes, with the former aiming to extract semantically meaningful knowledge of shapes and the latter focusing on generating plausible-looking shapes based on user inputs. Traditionally, shape analysis and synthesis are based on representations such as meshes, parameterisations, and Laplacians, which lead to mostly hand-crafted computation rules that are either suboptimal or treat related tasks separately. In this work, we propose to represent a 2D/3D shape as a square symmetric matrix that correlates every pair of geometric points on the shape, which allows us to formulate shape analysis and synthesis problems as principled optimisation problems that can be globally optimised. To demonstrate the usefulness of our new metric representation for shape analysis, we first address 3D mesh saliency detection by representing a shape as a pairwise feature distance matrix, whose principal eigenvector is experimentally shown to outperform the traditional saliency detection rules for capturing ground truth saliency annotations. Following this work, we then unify saliency detection and nonrigid shape matching via a jointly learned metric representation, which is shown to improve the accuracy of both tasks on the existing saliency detection and shape matching benchmarks. To also demonstrate the usefulness of our metric representation for shape synthesis, we address 2D facial shape beautification in images by representing a facial shape as the orthogonal projection matrix onto 2D facial landmarks, which is shown to improve the attractiveness of both frontal-neutral and non-frontal-non-neutral faces in the user studies. Finally, we show that adversarially learning the distributions of human shapes and poses in a hidden space produces higher quality human samples than in the geometry space. Together, these results show that our metric representation benefits both the analysis and synthesis of shapes, with the potential of unifying more diverse tasks such as part segmentation and labelling in the future work