This paper analyzes the discipline status of computer graphics and some problems in the teaching and the effect of scientific research in the course teaching and the teacher itself. In order to solve these problems, s...
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computer-Generated Holography (CGH) algorithms simulate numerical diffraction, being applied in particular for holographic display technology. Due to the wave-based nature of diffraction, CGH is highly computationally...
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computer-Generated Holography (CGH) algorithms simulate numerical diffraction, being applied in particular for holographic display technology. Due to the wave-based nature of diffraction, CGH is highly computationally intensive, making it especially challenging for driving high-resolution displays in real-time. To this end, we propose a technique for efficiently calculating holograms of 3D line segments. We express the solutions analytically and devise an efficiently computable approximation suitable for massively parallel computing architectures. The algorithms are implemented on a GPU (with CUDA), and we obtain a 70-fold speedup over the reference point-wise algorithm with almost imperceptible quality loss. We report real-time frame rates for CGH of complex 3D line-drawn objects, and validate the algorithm in both a simulation environment as well as on a holographic display setup.
In this study, we proposed a method to generate a more realistic image of buildings drawn by the three-point perspective method than the conventional perspective projection when the building is observed in real *** on...
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Skeleton-based action recognition algorithms have been widely applied to human action recognition. Graph convolutional networks (GCNs) generalize convolutional neural networks (CNNs) to non-Euclidean graphs and achiev...
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Skeleton-based action recognition algorithms have been widely applied to human action recognition. Graph convolutional networks (GCNs) generalize convolutional neural networks (CNNs) to non-Euclidean graphs and achieve significant performance in skeleton-based action recognition. However, existing GCN-based models have several issues, such as the topology of the graph is defined based on the natural skeleton of the human body, which is fixed during training, and it may not be applied to different layers of the GCN model and diverse datasets. Besides, the higher-order information of the joint data, for example, skeleton and dynamic information is not fully utilised. This work proposes a novel multi-stream adaptive spatial-temporal attention GCN model that overcomes the aforementioned issues. The method designs a learnable topology graph to adaptively adjust the connection relationship and strength, which is updated with training along with other network parameters. Simultaneously, the adaptive connection parameters are utilised to optimise the connection of the natural skeleton graph and the adaptive topology graph. The spatial-temporal attention module is embedded in each graph convolution layer to ensure that the network focuses on the more critical joints and frames. A multi-stream framework is built to integrate multiple inputs, which further improves the performance of the network. The final network achieves state-of-the-art performance on both the NTU-RGBD and Kinetics-Skeleton action recognition datasets. The simulation results prove that the proposed method reveals better results than existing methods in all perspectives and that shows the superiority of the proposed method.
On the one side, the formalism of Global Transformations comes with the claim of capturing any transformation of space that is local, synchronous and deterministic. The claim has been proven for different classes of m...
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Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by s...
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Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by scanning devices. State-of-the-art registration techniques still struggle when the overlap region between the two point clouds is small, and completely fail if there is no overlap between the scan pairs. In this article, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. The key idea is combining the registration and completion tasks in a way that reinforces each other. In particular, we simultaneously train the registration network and completion network using two coupled flows, one that register-and-complete, and one that complete-and-register, and encourage the two flows to produce a consistent result. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration, and hence to a better point cloud prediction that completes the registered scans. It is also worth mentioning that each of the components in our neural network outperforms state-of-the-art methods in both completion and registration. We further analyze our network with several ablation studies and demonstrate its performance on a large number of partial point clouds, both synthetic and real-world, that have only small or no overlap.
Hair rendering has been a focal point of attention in computer graphics for the last couple of decades. However, there have been few contributions to the modeling and rendering of the natural hair aging phenomenon. We...
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Hair rendering has been a focal point of attention in computer graphics for the last couple of decades. However, there have been few contributions to the modeling and rendering of the natural hair aging phenomenon. We present a new technique that simulates the process of hair graying and hair thinning on digital models due to aging. Given a 3D human head model with hair, we first compute a segmentation of the head using K-means since hair aging occurs at different rates in distinct head parts. Hair graying is simulated according to recent biological knowledge on aging factors for hairs, and hair thinning decreases hair diameters linearly with time. Our system is biologically inspired, supports facial hair, both genders and many ethnicities, and is compatible with different lengths of hair strands. Our real-time results resemble real-life hair aging, accomplished by simulating the stochastic nature of the process and the gradual decrease of melanin.
The technique of hiding knowledge in certain details is steganography. One of the main trends of computer infrastructure and connectivity following the advent of the Internet has been cyber protection and information ...
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The technique of hiding knowledge in certain details is steganography. One of the main trends of computer infrastructure and connectivity following the advent of the Internet has been cyber protection and information security. It is required to hide valuable information like passwords, bank details, and other personal documents. In this perspective, a novel algorithm is proposed for data hiding in digital videos using entropy-based blocks selection to make the message more secure. In which firstly random frames are selected by using a key and then random macroblocks are selected. The macroblocks with high entropy have chosen to hide the data in them. This paper presents a critical analysis driven from the literature and the experimental results. To quantify the results and to evaluate the performance of distinct steganography techniques, different quality metrics like peak signal-to-noise ratio (PSNR), mean squared error (MSE) & bit error rate (BER). have been used. Experimental results show that the proposed algorithm outperforms the other state of art techniques and also able to hide the secret message in the video without adding the noise and other distortions.
Modern graphics processing units come with dedicated hardware to perform ray/ triangle intersections and bounding volume hierarchy traversal. While the primary use case for this hardware is photorealistic 3-D computer...
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Modern graphics processing units come with dedicated hardware to perform ray/ triangle intersections and bounding volume hierarchy traversal. While the primary use case for this hardware is photorealistic 3-D computer graphics, with careful algorithm design scientists can also use this special-purpose hardware to accelerate general-purpose computations, such as point containment queries. This article explains the principles behind these techniques and their application to vector field visualization of large simulation data using particle tracing.
The Wisdom of Losing (Yourself) is a proposal for series of brief filmic artworks by multimedia artist Esther Rolinson. The intention in making the new works is to explore how change in our internal emotional landscap...
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