Simon Schreiberhuber — accepted AI-conference papers

2022

GigaDepth: Learning Depth from Structured Light with Branching Neural Networks

Simon Schreiberhuber, Jean-Baptiste Weibel, Timothy Patten, Markus Vincze

ECCV 2022poster

"Structured light-based depth sensors provide accurate depth information independently of the scene appearance by extracting pattern positions from the captured pixel intensities. Spatial neighborhood encoding, in particular, is a popular structured light approach for off-the-shelf hardware. However…

Cited by 7 Source ↗PDF ↗Scholar ↗

2019

ScalableFusion: High-resolution Mesh-based Real-time 3D Reconstruction

Simon Schreiberhuber, Johann Prankl, Timothy Patten, Markus Vincze

ICRA 2019poster

Dense 3D reconstructions generate globally consistent data of the environment suitable for many robot applications. Current RGB-D based reconstructions, however, only maintain the color resolution equal to the depth resolution of the used sensor. This firmly limits the precision and realism of the g…

Cited by 24 Source ↗Scholar ↗