1 citations · 1 across the 3 of their papers we have counts for
7 papers
Improved Convex Decomposition with Ensembling and Negative Primitives
Vaibhav Vavilala, Florian Kluger, Seemandhar Jain +3
Describing a scene in terms of primitives -- geometrically simple shapes that offer a parsimonious but accurate abstraction of structure -- is an established and difficult fitting…
Robust Shape Fitting for 3D Scene Abstraction
Florian Kluger, Eric Brachmann, Michael Ying Yang +1
Humans perceive and construct the world as an arrangement of simple parametric models. In particular, we can often describe man-made environments using volumetric primitives such a…
PARSAC: Accelerating Robust Multi-Model Fitting with Parallel Sample Consensus
Florian Kluger, Bodo Rosenhahn
We present a real-time method for robust estimation of multiple instances of geometric models from noisy data. Geometric models such as vanishing points, planar homographies or fun…
Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images
Florian Kluger, Hanno Ackermann, Eric Brachmann +2
Humans perceive and construct the surrounding world as an arrangement of simple parametric models. In particular, man-made environments commonly consist of volumetric primitives su…
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus
Florian Kluger, Eric Brachmann, Hanno Ackermann +3
We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements. Applications include finding multiple vanishing points in man-made scen…
Temporally Consistent Horizon Lines
Florian Kluger, Hanno Ackermann, Michael Ying Yang +1
The horizon line is an important geometric feature for many image processing and scene understanding tasks in computer vision. For instance, in navigation of autonomous vehicles or…
Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection
Florian Kluger, Hanno Ackermann, Michael Ying Yang +1
We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed sc…