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of 28
pro vyhledávání: '"Hillemann, Markus"'
Evaluation of Multi-task Uncertainties in Joint Semantic Segmentation and Monocular Depth Estimation
While a number of promising uncertainty quantification methods have been proposed to address the prevailing shortcomings of deep neural networks like overconfidence and lack of explainability, quantifying predictive uncertainties in the context of jo
Externí odkaz:
http://arxiv.org/abs/2405.17097
Autor:
Hillemann, Markus, Langendörfer, Robert, Heiken, Max, Mehltretter, Max, Schenk, Andreas, Weinmann, Martin, Hinz, Stefan, Heipke, Christian, Ulrich, Markus
Neural Radiance Fields (NeRFs) have become a rapidly growing research field with the potential to revolutionize typical photogrammetric workflows, such as those used for 3D scene reconstruction. As input, NeRFs require multi-view images with correspo
Externí odkaz:
http://arxiv.org/abs/2405.04345
Autor:
Jäger, Miriam, Kapler, Theodor, Feßenbecker, Michael, Birkelbach, Felix, Hillemann, Markus, Jutzi, Boris
In the fields of photogrammetry, computer vision and computer graphics, the task of neural 3D scene reconstruction has led to the exploration of various techniques. Among these, 3D Gaussian Splatting stands out for its explicit representation of scen
Externí odkaz:
http://arxiv.org/abs/2405.02005
The estimation of 6D object poses is a fundamental task in many computer vision applications. Particularly, in high risk scenarios such as human-robot interaction, industrial inspection, and automation, reliable pose estimates are crucial. In the las
Externí odkaz:
http://arxiv.org/abs/2403.07741
Quantifying the predictive uncertainty emerged as a possible solution to common challenges like overconfidence or lack of explainability and robustness of deep neural networks, albeit one that is often computationally expensive. Many real-world appli
Externí odkaz:
http://arxiv.org/abs/2402.10580
Deep neural networks have shown exceptional performance in various tasks, but their lack of robustness, reliability, and tendency to be overconfident pose challenges for their deployment in safety-critical applications like autonomous driving. In thi
Externí odkaz:
http://arxiv.org/abs/2307.09947
In many industrial processes, such as power generation, chemical production, and waste management, accurately monitoring industrial burner flame characteristics is crucial for safe and efficient operation. A key step involves separating the flames fr
Externí odkaz:
http://arxiv.org/abs/2306.14789
Deep neural networks lack interpretability and tend to be overconfident, which poses a serious problem in safety-critical applications like autonomous driving, medical imaging, or machine vision tasks with high demands on reliability. Quantifying the
Externí odkaz:
http://arxiv.org/abs/2303.09843
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