Probabilistic Parametric Curves For Sequence Modeling
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Probabilistic Parametric Curves for Sequence Modeling
Author: Hug, Ronny
language: en
Publisher: KIT Scientific Publishing
Release Date: 2022-07-12
This work proposes a probabilistic extension to Bézier curves as a basis for effectively modeling stochastic processes with a bounded index set. The proposed stochastic process model is based on Mixture Density Networks and Bézier curves with Gaussian random variables as control points. A key advantage of this model is given by the ability to generate multi-mode predictions in a single inference step, thus avoiding the need for Monte Carlo simulation.
Multimodal Panoptic Segmentation of 3D Point Clouds
Author: Dürr, Fabian
language: en
Publisher: KIT Scientific Publishing
Release Date: 2023-10-09
The understanding and interpretation of complex 3D environments is a key challenge of autonomous driving. Lidar sensors and their recorded point clouds are particularly interesting for this challenge since they provide accurate 3D information about the environment. This work presents a multimodal approach based on deep learning for panoptic segmentation of 3D point clouds. It builds upon and combines the three key aspects multi view architecture, temporal feature fusion, and deep sensor fusion.
Proceedings of the 2022 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory
Author: Beyerer, Jürgen
language: en
Publisher: KIT Scientific Publishing
Release Date: 2023-07-05
In August 2022, Fraunhofer IOSB and IES of KIT held a joint workshop in a Schwarzwaldhaus near Triberg. Doctoral students presented research reports and discussed various topics like computer vision, optical metrology, network security, usage control, and machine learning. This book compiles the workshop's results and ideas, offering a comprehensive overview of the research program of IES and Fraunhofer IOSB.