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Found 8 papers, 0 papers with code
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 joint semantic segmentation and monocular depth estimation has not been explored yet.
Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications
As input, NeRFs require multi-view images with corresponding camera poses as well as the interior orientation.
HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2
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.
Uncertainty Quantification with Deep Ensembles for 6D Object Pose Estimation
In this work, we propose a method to quantify the uncertainty of multi-stage 6D object pose estimation approaches with deep ensembles.
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation
By implicitly leveraging the predictive uncertainties of the teacher, EMUFormer achieves new state-of-the-art results on Cityscapes and NYUv2 and additionally estimates high-quality predictive uncertainties for both tasks that are comparable or superior to a Deep Ensemble despite being an order of magnitude more efficient.
U-CE: Uncertainty-aware Cross-Entropy for Semantic Segmentation
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.
Segmentation of Industrial Burner Flames: A Comparative Study from Traditional Image Processing to Machine and Deep Learning
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.
DUDES: Deep Uncertainty Distillation using Ensembles for Semantic Segmentation
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.
