Transit maps are diagrams designed to present information for using public transportation systems, such as urban railway networks. Creating a transit map is a time-consuming process, which requires iterative information selection, layout design, and usability validation, and thus maps cannot easily be customised or updated frequently. To improve this, scientists investigate fully or semi-automatic techniques in order to produce high quality transit maps using computers and further examine their corresponding usability. Nonetheless, the quality gap between manually-drawn maps and machine-generated maps is still large. To elaborate the current research status, this state-of-the-art report provides an overview of the transit map generation process, primarily from Design, Machine, and Human perspectives. A systematic categorisation is introduced to describe the design pipeline, and an extensive analysis of
perspectives is conducted to support the proposed taxonomy. We conclude this survey with a discussion on the current research status, open challenges, and future directions.
Gaussian Mixture Models (GMMs) are probability density functions based on a linear combination of normal distributions. We want to model the density of 3d point clouds using GMMs. This representation is used for point cloud processing tasks, such as registration or surface reconstruction. Our goal is to construct GMMs from point clouds specifically for applications in Deep Learning. In this thesis, different existing GMM construction algorithms are evaluated and new algorithms are designed to find the best algorithm for the given task. A gradient-descent-based method is currently in development. Additionally, a visualization tool is implemented aiding in reasoning about and inspection of three-dimensional GMMs.
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Institute of Visual Computing & Human-Centered Technology
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