Theodor Kapler
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View article: Evaluation of multi-task uncertainties in joint semantic segmentation and monocular depth estimation
Evaluation of multi-task uncertainties in joint semantic segmentation and monocular depth estimation Open
Deep neural networks achieve outstanding results in perception tasks such as semantic segmentation and monocular depth estimation, making them indispensable in safety-critical applications like autonomous driving and industrial inspection.…
View article: HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2
HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2 Open
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 representat…
View article: A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation
A Comparative Study on Multi-task Uncertainty Quantification in Semantic Segmentation and Monocular Depth Estimation Open
Deep neural networks excel in perception tasks such as semantic segmentation and monocular depth estimation, making them indispensable in safety-critical applications like autonomous driving and industrial inspection. However, they often s…
View article: HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2
HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2 Open
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 representat…
View article: Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation Open
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-…