New paradigms for navigation in and interaction with large captured data sets

ESR10

Objectives

In collaboration with CNR, the fellow will investigate visualization methods that go beyond classic rendering. Large captured data sets are often cluttered with noise and have high depth complexity, making them hard to navigate and to visualize the desired objects of interest. Therefore, the candidate will explore new navigation modes, also in the context of novel display environments that help the user stay within the desired navigational area (which should be determined automatically, which is challenging for point clouds). The candidate will explore efficient and scalable methods to exploit transparency to obtain novel views of large, cluttered data sets, for example by intelligently peeling away layers of the data to see beyond walls and obstacles.

Expected Results

Research reports and internal prototype implementation, as well as published papers on statistical data representation for 3D data sets.

Specific Requirements

The position requires prior knowlede in computer graphics, computer vision, or visual computing. Hence, the background needs to be a M.Sc. in Computer Science or a closely related field. Skills in programming (C/C++, Matlab, OpenGL) are required.

Host Institution

Supervisor: Prof. Michael Wimmer
Head of Rendering and Modeling Group Institute of Visual Computing & Human-Centered Technology
TU Wien
Austria

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