Dominik Hirner recieved the B.Sc. and M.Sc. degrees in Computer Science from the University of Technology Graz, Austria, in 2016 and 2018, respectively. During his Master's study he spent two years at the AIT Austrian Institute of Technology. There he worked on many customer projects but mainly focused on his work for his master thesis. His thesis was part of an industrial light-field camera used for high-speed in-line computation. In particular he worked on a Neural-Network that predicted surface normals as well as object classes using their epipolar geometry given by the light-field data.
Example result of the light-field NN for a glossy coin object. The color encodes the surface-normal gradient in x-direction.
Since November 2018, he is a Ph.D. student at the Institute of Computer Graphics and Vision (ICG), Graz University of Technology.
In 2019 he worked on the Core3D project together with General Electrics , Cornell and CMU. In this project he worked mainly on improving 3D reconstruction of urban scenes via satellite stereo images.
Example of a point-cloud reconstruction of an urban scene (Jacksonville FL, USA).
In 2020 he worked on the SV4I (street-view for the visually impaired) project. This FFG funded project was focused on using 3D data generated from structure-from-motion techniques in order to create a geo-referenced map for walkable and non-walkable areas and pure vision based localization.
His current research interest includes computer vision, image analysis and deep learning. In particular, he is dealing with lightweight CNNs for stereo vision and 3D reconstruction using deep learning techniques.
FC-DCNN network structure. A lightweight disparity estimation network. source-code: FC-DCNN
Disparity map results of the FC-DCNN network output. Left: Middlebury test image, Right: ETH3D test image
A full list of my publications can be found in the PURE system of TU Graz.
Friedrich Fraundorfer and Dominik HirnerFC-DCNN: A densely connected neural network for stereo estimationShow publication in PURE
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