Most current research on automatically captioning and describing scenes with spatial content focuses on images. We outline that generating descriptive text for a synthesized 3D scene can be achieved via a suitable int...
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Despite of exciting advances in image-based rendering and novel view synthesis, it is still challenging to achieve high-resolution results that can reach production-level quality when applying such methods to the task...
Despite of exciting advances in image-based rendering and novel view synthesis, it is still challenging to achieve high-resolution results that can reach production-level quality when applying such methods to the task of stereo conversion. At the same time, only very few dedicated stereo conversion approaches exist, which also fall short in terms of the required quality. Hence, in this paper, we present a novel method for high-resolution 2D-to-3D conversion. It is fully differentiable in all of its stages and performs disparity-informed warping, consistent foreground-background compositing, and background-aware inpainting. To enable temporal consistency in the resulting video, we propose a strategy to integrate information from additional video frames. Extensive ablation studies validate our design choices, leading to a fully automatic model that outperforms existing approaches by a large margin (49-70% LPIPS error reduction). Finally, inspired from current practices in manual stereo conversion, we introduce optional interactive tools into our model, which allow to steer the conversion process and make it significantly more applicable for 3D film production.
This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its i...
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Point-based geometry representations have become widely used in numerous contexts,ranging from particle-based simulations,over stereo image matching,to depth sensing via light detection and *** application focus is on...
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Point-based geometry representations have become widely used in numerous contexts,ranging from particle-based simulations,over stereo image matching,to depth sensing via light detection and *** application focus is on the reconstruction of curved line structures in noisy 3D point cloud *** algorithms operating on such point clouds often rely on the notion of a local *** the latter,our approach employs multi-scale neighborhoods,for which weighted covariance measures of local points are *** line structures are reconstructed via vector field tracing,using a bidirectional piecewise streamline *** also introduce an automatic selection of optimal starting points via multi-scale geometric *** pipeline development and choice of parameters was driven by an extensive,automated initial analysis process on over a million prototype test *** behavior of our approach is controlled by several parameters—the majority being set automatically,leaving only three to be controlled by a *** an extensive,automated final evaluation,we cover over one hundred thousand parameter sets,including 3D test geometries with varying curvature,sharp corners,intersections,data holes,and systematically applied varying types of ***,we analyzed different choices for the point of reference in the co-variance computation;using a weighted mean performed best in most *** addition,we compared our method to current,publicly available line reconstruction *** to thirty times faster execution times were achieved in some cases,at comparable error ***,we also demonstrate an exemplary application on four real-world 3D light detection and ranging datasets,extracting power line cables.
Explainable AI (XAI) is a rapidly growing domain with a myriad of proposed methods as well as metrics aiming to evaluate their efficacy. However, current studies are often of limited scope, examining only a handful of...
ISBN:
(纸本)9798331314385
Explainable AI (XAI) is a rapidly growing domain with a myriad of proposed methods as well as metrics aiming to evaluate their efficacy. However, current studies are often of limited scope, examining only a handful of XAI methods and ignoring underlying design parameters for performance, such as the model architecture or the nature of input data. Moreover, they often rely on one or a few metrics and neglect thorough validation, increasing the risk of selection bias and ignoring discrepancies among metrics. These shortcomings leave practitioners confused about which method to choose for their problem. In response, we introduce LATEC, a large-scale benchmark that critically evaluates 17 prominent XAI methods using 20 distinct metrics. We systematically incorporate vital design parameters like varied architectures and diverse input modalities, resulting in 7,560 examined combinations. Through LATEC, we showcase the high risk of conflicting metrics leading to unreliable rankings and consequently propose a more robust evaluation scheme. Further, we comprehensively evaluate various XAI methods to assist practitioners in selecting appropriate methods aligning with their needs. Curiously, the emerging top-performing method, Expected Gradients, is not examined in any relevant related study. LATEC reinforces its role in future XAI research by publicly releasing all 326k saliency maps and 378k metric scores as a (meta-)evaluation dataset. The benchmark is hosted at: https://***/IML-DKFZ/latec.
Measurement-based quantum computing (MBQC) is a promising approach to reducing circuit depth in noisy intermediate-scale quantum algorithms such as the Variational Quantum Eigensolver (VQE). Unlike gate-based computin...
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The scale of the global cloud computing market is growing rapidly. In the context of the global digital economy, cloud computing has become an inevitable choice for digital transformation of enterprises. With the rapi...
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The motive of current investigations provides the numerical solutions of the neuro computing solver based on the Levenberg-Marquardt backpropagation neural network approach (LMB) to solve the Zika virus system of rese...
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This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone’s center is vital for predicting its inte...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone’s center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network’s capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from $86 \%$ to $92 \%$ or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting.
Point sets are a widely used spatial data structure in computational and observational domains, e.g. in physics particle simulations, computergraphics or remote sensing. Algorithms typically operate in local neighbor...
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