most citedx3ogre: Connecting X3D to a state of the art rendering engine

1 citations · 2 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2020

Style-transfer GANs for bridging the domain gap in synthetic pose estimator training

Pavel Rojtberg, Thomas Pöllabauer, Arjan Kuijper

Given the dependency of current CNN architectures on a large training set, the possibility of using synthetic data is alluring as it allows generating a virtually infinite amount o…

cs.CV2019

Real-time texturing for 6D object instance detection from RGB Images

Pavel Rojtberg, Arjan Kuijper

For objected detection, the availability of color cues strongly influences detection rates and is even a prerequisite for many methods. However, when training on synthetic CAD data…

cs.CV2019

calibDB: enabling web based computer vision through on-the-fly camera calibration

Pavel Rojtberg, Felix Gorschlüter

For many computer vision applications, the availability of camera calibration data is crucial as overall quality heavily depends on it. While calibration data is available on some…

cs.HC2019

User Guidance for Interactive Camera Calibration

Pavel Rojtberg

For building a Augmented Reality (AR) pipeline, the most crucial step is the camera calibration as overall quality heavily depends on it. In turn camera calibration itself is influ…

cs.CV20191 cited

Efficient Pose Selection for Interactive Camera Calibration

Pavel Rojtberg, Arjan Kuijper

The choice of poses for camera calibration with planar patterns is only rarely considered - yet the calibration precision heavily depends on it. This work presents a pose selection…

cs.GR20191 cited

x3ogre: Connecting X3D to a state of the art rendering engine

Pavel Rojtberg, Benjamin Audenrith

We connect X3D to the state of the art OGRE renderer using our prototypical x3ogre implementation. At this we perform a comparison of both on a conceptual level, highlighting simil…