Thesis presentations

Zeit: 22.06.2018, 10:00 Uhr
Ort: Raum 103 im Fraunhofer IGD, Fraunhoferstrasse 5, S3|05
Referent: Johannes Heilmann (Betreuer: Harald Wüst)
Titel: "Visual-Inertial Model Target Tracking for Consumer Hardware" (Bachelorarbeit)
Abstract: This thesis explores visual-inertial tracking for the application in Augmented Reality. Combining vision based tracking with data from inertial sensors like accelerometer and gyroscope can result in a faster and more robust tracking system. The two types of data complement each other well.
Tracking results in the form of 2D/3D correspondences from either a poster tracker or model tracker are combined with inertial data from the sensors of a Surface Pro 2 and fused in an Extended Kalman Filter. Different configurations are available. On the vision side there is a poster tracker using FAST and BRIEF for matching and the KLT for tracking. Alternatively a model tracker can be used. It builds a line model from a CAD model and tracks points on the lines in the image. On the inertial sensor side there are also two options. Either only gyroscope measurements can be used, or data from the gyroscope and an accelerometer can
be included.
The system is built to be used with consumer hardware. For this thesis a Microsoft Surface Pro 2 was used. This means that the data is not synchronised to a common clock and the inertial sensors are less accurate than those found in specialised hardware. The system is evaluated on a recorded image sequence and corresponding sensor data. The different parameters of the EKF models are tuned by experimentally minimising the RSME between EKF estimations and accurate baseline results.
It is shown that combining visual and inertial data allows the vision system to rely on tracking less features. This results in reduced computational cost. But the inertial sensors of the Surface Pro 2 are not accurate enough to allow a camera pose estimation based on inertial data alone in case the camera tracking fails.

Zeit: 29.06.2018, 10:00 Uhr
Ort: Raum 074 im Fraunhofer IGD, Fraunhoferstrasse 5, S3|05
Referent: Patrick Seeman (Betreuer: Daniel Thuerck)
Titel: "Soft Transparency for Point Cloud Rendering" (Masterarbeit)
Abstract: We propose a novel rendering framework for visualizing point data with complex structures or different quality of data. We characterize a point cloud using a per-point scalar field for differentiating parts of the dataset, i.e. based on the uncertainty of the points given by local normal variation or point density. Our rendering method uses the scalar field to render points as solid splats or semi-transparent spheres with non-uniform density to produce the final image. To that end, we derive a base model for integrating density in (potentially intersecting) spheres for both the uniform and non-uniform setting and introduce a simplified and fast approximation which yields interactive rendering speeds for millions of points. Having our method only rely on basic rasterization operations enables users to adjust rendering properties in real-time. The resulting interactive rendering of differently characterized point data leverages a clearer understanding of scenes in comparison with previous point splatting techniques and basic transparency rendering. Tests on several datasets with different characteristics and user studies substantiate our goal of easier scene understanding.

Zeit: 29.06.2018, 11:00 Uhr
Ort: Raum 074 im Fraunhofer IGD, Fraunhoferstrasse 5, S3|05
Referent: Tobias Dollenbacher (Betreuer: Daniel Thuerck)
Titel: "Patterns for the Distributed Solution of General MRF MAP problems by Dynamic Decomposition" (Bachelorarbeit)
Abstract: A versatile tool for models in computer vision problems are Markov Random Fields. Since the solving of MAP problems on general pairwise Markov Random Fields is NP- hard, the big computational requirement for large Markov Random Fields make the solving process time consuming. The availability of high resolution images and cheap mass storage grow the input data for MRF MAP solvers. Hence, the need of an effi- cient solver that can deal with large general pairwise MRFs is stressed. In this thesis an approach to create such a solver by distributed > processing of huge MRF MAP problems using the mapMAP solver is described. Different patterns are introduced and used to dis- tribute the optimization data amongst the processes. The performance of the different strategies is evaluated and its advantages and weaknesses are shown. The underlying implementation uses MPI for the communication between the processes.


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