Publications

Year: Author:

Max Lyon, Marcel Campen, David Bommes, Leif Kobbelt
SIGGRAPH 2019

The generation of quad meshes based on surface parametrization techniques has proven to be a versatile approach. These techniques quantize an initial seamless parametrization so as to obtain an integer grid map implying a pure quad mesh. State-of-the-art methods following this approach have to assume that the surface to be meshed either has no boundary, or has a boundary which the resulting mesh is supposed to be aligned to. In a variety of applications this is not desirable and non-boundary-aligned meshes or grid-parametrizations are preferred. We thus present a technique to robustly generate integer grid maps which are either boundary-aligned, non-boundary-aligned, or partially boundary-aligned, just as required by different applications. We thereby generalize previous work to this broader setting. This enables the reliable generation of trimmed quad meshes with partial elements along the boundary, preferable in various scenarios, from tiled texturing over design and modeling to fabrication and architecture, due to fewer constraints and hence higher overall mesh quality and other benefits in terms of aesthetics and flexibility.





Javor Kalojanov, Isaak Lim, Niloy Mitra, Leif Kobbelt
Computer Graphics Forum (Proc. EUROGRAPHICS 2019)

We propose a novel method to synthesize geometric models from a given class of context-aware structured shapes such as buildings and other man-made objects. Our central idea is to leverage powerful machine learning methods from the area of natural language processing for this task. To this end, we propose a technique that maps shapes to strings and vice versa, through an intermediate shape graph representation. We then convert procedurally generated shape repositories into text databases that in turn can be used to train a variational autoencoder which enables higher level shape manipulation and synthesis like, e.g., interpolation and sampling via its continuous latent space.

» Show BibTeX

@article{Kalojanov2019,
journal = {Computer Graphics Forum},
title = {{String-Based Synthesis of Structured Shapes}},
author = {Javor Kalojanov and Isaak Lim and Niloy Mitra and Leif Kobbelt},
pages = {027-036},
volume= {38},
number= {2},
year = {2019},
note = {\URL{https://diglib.eg.org/bitstream/handle/10.1111/cgf13616/v38i2pp027-036.pdf}},
DOI = {10.1111/cgf.13616},
}






Isaak Lim, Moritz Ibing, Leif Kobbelt
Eurographics Symposium on Geometry Processing 2019

Automatic synthesis of high quality 3D shapes is an ongoing and challenging area of research. While several data-driven methods have been proposed that make use of neural networks to generate 3D shapes, none of them reach the level of quality that deep learning synthesis approaches for images provide. In this work we present a method for a convolutional point cloud decoder/generator that makes use of recent advances in the domain of image synthesis. Namely, we use Adaptive Instance Normalization and offer an intuition on why it can improve training. Furthermore, we propose extensions to the minimization of the commonly used Chamfer distance for auto-encoding point clouds. In addition, we show that careful sampling is important both for the input geometry and in our point cloud generation process to improve results. The results are evaluated in an auto-encoding setup to offer both qualitative and quantitative analysis. The proposed decoder is validated by an extensive ablation study and is able to outperform current state of the art results in a number of experiments. We show the applicability of our method in the fields of point cloud upsampling, single view reconstruction, and shape synthesis.

» Show BibTeX

@article{Lim:2019:ConvolutionalDecoder,
author = "Lim, Isaak and Ibing, Moritz and Kobbelt, Leif",
title = "A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization",
journal = "Computer Graphics Forum",
volume = 38,
number = 5,
year = 2019
}






Andreas Tillmann
Computational Optimization and Applications

We investigate the NP-hard problem of computing the spark of a matrix (i.e., the smallest number of linearly dependent columns), a key parameter in compressed sensing and sparse signal recovery. To that end, we identify polynomially solvable special cases, gather upper and lower bounding procedures, and propose several exact (mixed-)integer programming models and linear programming heuristics. In particular, we develop a branch-and-cut scheme to determine the girth of a matroid, focussing on the vector matroid case, for which the girth is precisely the spark of the representation matrix. Extensive numerical experiments demonstrate the effectiveness of our specialized algorithms compared to general-purpose black-box solvers applied to several mixed-integer programming models.



Code and test instances available per request; will become directly available on this page in the near future.
» Show BibTeX

@article{Tillmann2019,
author = {Andreas M. Tillmann},
title = {{Computing the Spark: Mixed-Integer Programming\\for the (Vector) Matroid Girth Problem}},
journal = {{Computational Optimization and Applications}},
volume = {to appear},
year = {2019}
}






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