Publications

Year: Author:

Aljoša Ošep, Wolfgang Mehner, Paul Voigtlaender, Bastian Leibe
Accepted for IEEE Int. Conference on Robotics and Automation (ICRA'18), to appear

The most common paradigm for vision-based multi-object tracking is tracking-by-detection, due to the availability of reliable detectors for several important object categories such as cars and pedestrians. However, future mobile systems will need a capability to cope with rich human-made environments, in which obtaining detectors for every possible object category would be infeasible. In this paper, we propose a model-free multi-object tracking approach that uses a category-agnostic image segmentation method to track objects. We present an efficient segmentation mask-based tracker which associates pixel-precise masks reported by the segmentation. Our approach can utilize semantic information whenever it is available for classifying objects at the track level, while retaining the capability to track generic unknown objects in the absence of such information. We demonstrate experimentally that our approach achieves performance comparable to state-of-the-art tracking-by-detection methods for popular object categories such as cars and pedestrians. Additionally, we show that the proposed method can discover and robustly track a large variety of other objects.

» Show BibTeX

@article{Osep18ICRA,
author = {O\v{s}ep, Aljo\v{s}a and Mehner, Wolfgang and Voigtlaender, Paul and Leibe, Bastian},
title = {Track, then Decide: Category-Agnostic Vision-based Multi-Object Tracking},
journal = {ICRA},
year = {2018}
}






Anne Gehre, Isaak Lim, Leif Kobbelt
Computer Graphics Forum (Proc. EUROGRAPHICS 2018)

Feature curves on 3D shapes provide important hints about significant parts of the geometry and reveal their underlying structure. However, when we process real world data, automatically detected feature curves are affected by measurement uncertainty, missing data, and sampling resolution, leading to noisy, fragmented, and incomplete feature curve networks. These artifacts make further processing unreliable. In this paper we analyze the global co-occurrence information in noisy feature curve networks to fill in missing data and suppress weakly supported feature curves. For this we propose an unsupervised approach to find meaningful structure within the incomplete data by detecting multiple occurrences of feature curve configurations (co-occurrence analysis). We cluster and merge these into feature curve templates, which we leverage to identify strongly supported feature curve segments as well as to complete missing data in the feature curve network. In the presence of significant noise, previous approaches had to resort to user input, while our method performs fully automatic feature curve co-completion. Finding feature reoccurrences however, is challenging since naive feature curve comparison fails in this setting due to fragmentation and partial overlaps of curve segments. To tackle this problem we propose a robust method for partial curve matching. This provides us with the means to apply symmetry detection methods to identify co-occurring configurations. Finally, Bayesian model selection enables us to detect and group re-occurrences that describe the data well and with low redundancy.

» Show BibTeX

@inproceedings{gehre2018feature,
title={Feature Curve Co-Completion in Noisy Data},
author={Gehre, Anne and Lim, Isaak and Kobbelt, Leif},
booktitle={Computer Graphics Forum},
volume={37},
number={2},
year={2018},
organization={Wiley Online Library}
}






Marcel Weiler, Dan Koschier, Magnus Brand, Jan Bender
Computer Graphics Forum (Eurographics)

In this paper, we present a novel physically consistent implicit solver for the simulation of highly viscous fluids using the Smoothed Particle Hydrodynamics (SPH) formalism. Our method is the result of a theoretical and practical in-depth analysis of the most recent implicit SPH solvers for viscous materials. Based on our findings, we developed a list of requirements that are vital to produce a realistic motion of a viscous fluid. These essential requirements include momentum conservation, a physically meaningful behavior under temporal and spatial refinement, the absence of ghost forces induced by spurious viscosities and the ability to reproduce complex physical effects that can be observed in nature. On the basis of several theoretical analyses, quantitative academic comparisons and complex visual experiments we show that none of the recent approaches is able to satisfy all requirements. In contrast, our proposed method meets all demands and therefore produces realistic animations in highly complex scenarios. We demonstrate that our solver outperforms former approaches in terms of physical accuracy and memory consumption while it is comparable in terms of computational performance. In addition to the implicit viscosity solver, we present a method to simulate melting objects. Therefore, we generalize the viscosity model to a spatially varying viscosity field and provide an SPH discretization of the heat equation.

» Show BibTeX

@article{WKBB2018,
author = {Marcel Weiler and Dan Koschier and Magnus Brand and Jan Bender},
title = {A Physically Consistent Implicit Viscosity Solver for SPH Fluids},
year = {2018},
journal = {Computer Graphics Forum (Eurographics)},
volume = {37},
number = {2}
}






Crispin Deul, Tassilo Kugelstadt, Marcel Weiler, Jan Bender
Computer Graphics Forum

In this paper, we present a novel direct solver for the efficient simulation of stiff, inextensible elastic rods within the Position-Based Dynamics (PBD) framework. It is based on the XPBD algorithm, which extends PBD to simulate elastic objects with physically meaningful material parameters. XPBD approximates an implicit Euler integration and solves the system of non-linear equations using a non-linear Gauss-Seidel solver. However, this solver requires many iterations to converge for complex models and if convergence is not reached, the material becomes too soft. In contrast we use Newton iterations in combination with our direct solver to solve the non-linear equations which significantly improves convergence by solving all constraints of an acyclic structure (tree), simultaneously. Our solver only requires a few Newton iterations to achieve high stiffness and inextensibility. We model inextensible rods and trees using rigid segments connected by constraints. Bending and twisting constraints are derived from the well-established Cosserat model. The high performance of our solver is demonstrated in highly realistic simulations of rods consisting of multiple ten-thousand segments. In summary, our method allows the efficient simulation of stiff rods in the Position-Based Dynamics framework with a speedup of two orders of magnitude compared to the original XPBD approach.

» Show BibTeX

@article{DKWB2018,
author = {Crispin Deul and Tassilo Kugelstadt and Marcel Weiler and Jan Bender},
title = {Direct Position-Based Solver for Stiff Rods},
year = {2018},
journal = {Computer Graphics Forum}
}






Sevinc Eroglu, Sascha Gebhardt, Patric Schmitz, Dominik Rausch, Torsten Wolfgang Kuhlen
Proceedings of IEEE Virtual Reality Conference 2018

Fluid artwork refers to works of art based on the aesthetics of fluid motion, such as smoke photography, ink injection into water, and paper marbling. Inspired by such types of art, we created Fluid Sketching as a novel medium for creating 3D fluid artwork in immersive virtual environments. It allows artists to draw 3D fluid-like sketches and manipulate them via six degrees of freedom input devices. Different sets of brush strokes are available, varying different characteristics of the fluid. Because of fluid's nature, the diffusion of the drawn fluid sketch is animated, and artists have control over altering the fluid properties and stopping the diffusion process whenever they are satisfied with the current result. Furthermore, they can shape the drawn sketch by directly interacting with it, either with their hand or by blowing into the fluid. We rely on particle advection via curl-noise as a fast procedural method for animating the fluid flow.




Patric Schmitz, Julian Romeo Hildebrandt, André Calero Valdez, Leif Kobbelt, Martina Ziefle
IEEE Transactions on Visualization and Computer Graphics

In virtual environments, the space that can be explored by real walking is limited by the size of the tracked area. To enable unimpeded walking through large virtual spaces in small real-world surroundings, redirection techniques are used. These unnoticeably manipulate the user’s virtual walking trajectory. It is important to know how strongly such techniques can be applied without the user noticing the manipulation—or getting cybersick. Previously, this was estimated by measuring a detection threshold (DT) in highly-controlled psychophysical studies, which experimentally isolate the effect but do not aim for perceived immersion in the context of VR applications. While these studies suggest that only relatively low degrees of manipulation are tolerable, we claim that, besides establishing detection thresholds, it is important to know when the user’s immersion breaks. We hypothesize that the degree of unnoticed manipulation is significantly different from the detection threshold when the user is immersed in a task. We conducted three studies: a) to devise an experimental paradigm to measure the threshold of limited immersion (TLI), b) to measure the TLI for slowly decreasing and increasing rotation gains, and c) to establish a baseline of cybersickness for our experimental setup. For rotation gains greater than 1.0, we found that immersion breaks quite late after the gain is detectable. However, for gains lesser than 1.0, some users reported a break of immersion even before established detection thresholds were reached. Apparently, the developed metric measures an additional quality of user experience. This article contributes to the development of effective spatial compression methods by utilizing the break of immersion as a benchmark for redirection techniques.





Tobias Fischer, Ganapati Hedge, Frederic Matter, Marius Pesavento, Marc Pfetsch, Andreas Tillmann
to appear in Proc. WSA (ITG Workshop on Smart Antennas) 2018

In this paper, we consider the problem of joint antenna selection and analog beamformer design in a downlink single-group multicast network. Our objective is to reduce the hardware requirement by minimizing the number of required phase shifters at the transmitter while fulfilling given quality of constraints. We formulate the problem as an L0 minimization problem and devise a novel branch-and-cut based algorithm to solve the formulated mixed-integer nonlinear program optimally. We also propose a suboptimal heuristic algorithm to solve the above problem with a low computational complexity. Furthermore, the performance of the suboptimal method is evaluated against the developed optimal method, which serves as a benchmark.

» Show BibTeX

@inproceedings{FischerHedgeMatterPesaventoPfetschTillmann2018,
author = {T. Fischer and G. Hedge and F. Matter and M. Pesavento and M. E. Pfetsch and A. M. Tillmann},
title = {{Joint Antenna Selection and Phase-Only Beam Using Mixed-Integer Nonlinear Programming}},
booktitle = {{Proc. WSA 2018}},
pages = {},
year = {2018},
note = {to appear}
}





Aljoša Ošep, Paul Voigtlaender, Jonathon Luiten, Stefan Breuers, Bastian Leibe
arXiv:1712.08832

We explore object discovery and detector adaptation based on unlabeled video sequences captured from a mobile platform. We propose a fully automatic approach for object mining from video which builds upon a generic object tracking approach. By applying this method to three large video datasets from autonomous driving and mobile robotics scenarios, we demonstrate its robustness and generality. Based on the object mining results, we propose a novel approach for unsupervised object discovery by appearance-based clustering. We show that this approach successfully discovers interesting objects relevant to driving scenarios. In addition, we perform self-supervised detector adaptation in order to improve detection performance on the KITTI dataset for existing categories. Our approach has direct relevance for enabling large-scale object learning for autonomous driving.

» Show BibTeX

@article{OsepVoigtlaender18arxiv,
title={Large-Scale Object Discovery and Detector Adaptation from Unlabeled Video},
author={Aljo\v{s}a O\v{s}ep and Paul Voigtlaender and Jonathon Luiten and Stefan Breuers and Bastian Leibe},
journal={arXiv preprint arXiv:1712.08832},
year={2018}
}






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