Cross-sectional line calculation and scan path selection are the key control aspects in laser marking. This paper introduces two basic ways of hatching for non nested single-layer graphics in the process of laser mark...
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A fast algorithm for generating pseudo first order geometrically differentiable surfaces for arbitrary polygonal meshes by using parametric bicubic spline interpolation, to be called PACSIS, is established. It can be ...
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To address the challenge of rapid geometric model development in the digital twin industry, this paper presents a comprehensive pipeline for constructing 3D models from images using monocular vision imaging principles...
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To address the challenge of rapid geometric model development in the digital twin industry, this paper presents a comprehensive pipeline for constructing 3D models from images using monocular vision imaging principles. Firstly, a structure-from-motion (SFM) algorithm generates a 3D point cloud from photographs. The feature detection methods scale-invariant feature transform (SIFT), speeded-up robust features (SURF), and KAZE are compared across six datasets, with SIFT proving the most effective (matching rate higher than 0.12). Using K-nearest-neighbor matching and random sample consensus (RANSAC), refined feature point matching and 3D spatial representation are achieved via antipodal geometry. Then, the Poisson surface reconstruction algorithm converts the point cloud into a mesh model. Additionally, texture images are enhanced by leveraging a visual geometry group (VGG) network-based deep learning approach. Content images from a dataset provide geometric contours via higher-level VGG layers, while textures from style images are extracted using the lower-level layers. These are fused to create texture-transferred images, where the image quality assessment (IQA) metrics SSIM and PSNR are used to evaluate texture-enhanced images. Finally, texture mapping integrates the enhanced textures with the mesh model, improving the scene representation with enhanced texture. The method presented in this paper surpassed a LiDAR-based reconstruction approach by 20% in terms of point cloud density and number of model facets, while the hardware cost was only 1% of that associated with LiDAR.
The story of Pixar doesn't start with its founding - a tech company's story rarely does. Rather, the original spark often comes from a university lab, a renegade group at a large company, or a hobbyist buildin...
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Cracks can develop in high-rise buildings because of long-term environmental changes and extreme loading events such as strong winds or earthquakes. Although deep learning-based identification methods can efficiently ...
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Cracks can develop in high-rise buildings because of long-term environmental changes and extreme loading events such as strong winds or earthquakes. Although deep learning-based identification methods can efficiently identify cracks, the accuracy of crack identification in high-rise buildings needs to be improved due to the lack of crack datasets specifically related to high-rise structures. Moreover, the number of available images of cracks in high-rise is limited. To this end, this paper establishes an intelligent crack identification method based on a photorealistic synthetic modeling technique. First, a computer graphics (CG) model of a high-rise building with assumed damage is constructed. Subsequently, the CG model is utilized to generate a dataset that includes photorealistic images of the high-rise building as well as corresponding labels for various components and types of damage. The generated dataset is then used to train a DeepLabv3 + neural network for structural component and damage identification, followed by validation by employing images of both synthetic and full-scale high-rise buildings. The trained network can accurately identify different components in images of the full-scale, high-rise building and identify cracks that are intentionally synthesized in those images. The results show that the synthetic dataset generated by the CG model not only allows for fast and efficient labeling for the purpose of neural network training but also outperforms methods that do not consider any application-specific context in crack identification.
This article describes a system for designing floodlighting of objects using computer graphics. Contrary to the currently used visualisation tools, the developed computer application is based on the daytime photograph...
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This article describes a system for designing floodlighting of objects using computer graphics. Contrary to the currently used visualisation tools, the developed computer application is based on the daytime photography of the object and not on its three-dimensional geometric model. The advantage of the system is the high photorealism of the simulation, with no need to create a collage of visualisation with photography. The designer uses the photometric data of luminaires, and their photometric and colourimetric parameters are defined. With the system it is possible to perform a precise lighting analysis-the distribution of illuminance and luminance-both for the entire facility and in any plane or point. The system also analyses the total installed power of a given design solution. The system application is presented as an example in the form of a case study. This example shows the features of the system and further expected directions of development.
The 2022 CHCCS Achievement Award from the Canadian Human-computer Communications Society is presented to Hao (Richard) Zhang from Simon Fraser University (SFU) for his numerous high-impact contributions to computer gr...
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This paper provides a comprehensive overview of approaches to the determination of isocontours and isosurfaces from given data sets. Different algorithms are reported in the literature for this purpose, which originat...
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This paper provides a comprehensive overview of approaches to the determination of isocontours and isosurfaces from given data sets. Different algorithms are reported in the literature for this purpose, which originate from various application areas, such as computer graphics or medical imaging procedures. In all these applications, the challenge is to extract surfaces with a specific isovalue from a given characteristic, so called isosurfaces. These different application areas have given rise to solution approaches that all solve the problem of isocontouring in their own way. Based on the literature, the following four dominant methods can be identified: the marching cubes algorithms, the tessellation-based algorithms, the surface nets algorithms and the ray tracing algorithms. With regard to their application, it can be seen that the methods are mainly used in the fields of medical imaging, computer graphics and the visualization of simulation results. In our work, we provide a broad and compact overview of the common methods that are currently used in terms of isocontouring with respect to certain criteria and their individual limitations. In this context, we discuss the individual methods and identify possible future research directions in the field of isocontouring.
The goal of digital dress body animation is to produce the most realistic dress body animation possible. Although a method based on the same topology as the body can produce realistic results, it can only be applied t...
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The goal of digital dress body animation is to produce the most realistic dress body animation possible. Although a method based on the same topology as the body can produce realistic results, it can only be applied to garments with the same topology as the body. Although the generalization-based approach can be extended to different types of garment templates, it still produces effects far from reality. We propose GSNet, a learning-based model that generates realistic garment animations and applies to garment types that do not match the body topology. We encode garment templates and body motions into latent space and use graph convolution to transfer body motion information to garment templates to drive garment motions. Our model considers temporal dependency and provides reliable physical constraints to make the generated animations more realistic. Qualitative and quantitative experiments show that our approach achieves state-of-the-art 3D garment animation performance.
This paper introduces a geometric generalization of signed distance fields for plane curves. We propose to store simplified geometric proxies to the curve at every sample. These proxies are constructed based on the di...
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This paper introduces a geometric generalization of signed distance fields for plane curves. We propose to store simplified geometric proxies to the curve at every sample. These proxies are constructed based on the differential geometric quantities of the represented curve and are used for queries such as closest point and distance calculations. We derive the theoretical approximation order of these constructs and provide empirical comparisons between geometric and algebraic distance fields of higher order. We validate our theoretical results by applying them to font representation and rendering.
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