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Publications


 

Layout Embedding via Combinatorial Optimization


Janis Born, Patrick Schmidt, Leif Kobbelt
Eurographics 2021
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We consider the problem of injectively embedding a given graph connectivity (a layout) into a target surface. Starting from prescribed positions of layout vertices, the task is to embed all layout edges as intersection-free paths on the surface. Besides merely geometric choices (the shape of paths) this problem is especially challenging due to its topological degrees of freedom (how to route paths around layout vertices). The problem is typically addressed through a sequence of shortest path insertions, ordered by a greedy heuristic. Such insertion sequences are not guaranteed to be optimal: Early path insertions can potentially force later paths into unexpected homotopy classes. We show how common greedy methods can easily produce embeddings of dramatically bad quality, rendering such methods unsuitable for automatic processing pipelines. Instead, we strive to find the optimal order of insertions, i.e. the one that minimizes the total path length of the embedding. We demonstrate that, despite the vast combinatorial solution space, this problem can be effectively solved on simply-connected domains via a custom-tailored branch-and-bound strategy. This enables directly using the resulting embeddings in downstream applications which cannot recover from initializations in a wrong homotopy class. We demonstrate the robustness of our method on a shape dataset by embedding a common template layout per category, and show applications in quad meshing and inter-surface mapping.

» Show BibTeX

@article{born2021layout,
title={Layout Embedding via Combinatorial Optimization},
author={Born, Janis and Schmidt, Patrick and Kobbelt, Leif},
year={2021},
journal={Computer Graphics Forum},
volume={40},
number={2},
}





MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation


István Sárándi, Timm Linder, Kai Oliver Arras, Bastian Leibe
IEEE Transactions on Biometrics, Behavior, and Identity Science (T-BIOM), Selected Best Works From Automatic Face and Gesture Recognition 2020 (to appear)
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Heatmap representations have formed the basis of human pose estimation systems for many years, and their extension to 3D has been a fruitful line of recent research. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and Z to metric depth around the subject. To obtain metric-scale predictions, 2.5D methods need a separate post-processing step to resolve scale ambiguity. Further, they cannot localize body joints outside the image boundaries, leading to incomplete estimates for truncated images. To address these limitations, we propose metric-scale truncation-robust (MeTRo) volumetric heatmaps, whose dimensions are all defined in metric 3D space, instead of being aligned with image space. This reinterpretation of heatmap dimensions allows us to directly estimate complete, metric-scale poses without test-time knowledge of distance or relying on anthropometric heuristics, such as bone lengths. To further demonstrate the utility our representation, we present a differentiable combination of our 3D metric-scale heatmaps with 2D image-space ones to estimate absolute 3D pose (our MeTRAbs architecture). We find that supervision via absolute pose loss is crucial for accurate non-root-relative localization. Using a ResNet-50 backbone without further learned layers, we obtain state-of-the-art results on Human3.6M, MPI-INF-3DHP and MuPoTS-3D. Our code is publicly available to facilitate further research.



Winning submission at the ECCV 2020 3D Poses in the Wild Challenge
» Show BibTeX

@article{sarandi2020metrabs,
title={MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absoute 3{D} Human Pose Estimation},
author={Istv\'an S\'ar\'andi and Timm Linder and Kai O. Arras and Bastian Leibe},
journal={IEEE Transactions on Biometrics, Behavior, and Identity Science},
note={in press}
}





Design and Evaluation of a Free-Hand VR-based Authoring Environment for Automated Vehicle Testing


Sevinc Eroglu, Frederic Stefan, Alain Chevalier, Daniel Roettger, Daniel Zielasko, Torsten Wolfgang Kuhlen, Benjamin Weyers
IEEE Conference on Virtual Reality and 3D User Interfaces 2021
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Virtual Reality is increasingly used for safe evaluation and validation of autonomous vehicles by automotive engineers. However, the design and creation of virtual testing environments is a cumbersome process. Engineers are bound to utilize desktop-based authoring tools, and a high level of expertise is necessary. By performing scene authoring entirely inside VR, faster design iterations become possible. To this end, we propose a VR authoring environment that enables engineers to design road networks and traffic scenarios for automated vehicle testing based on free-hand interaction. We present a 3D interaction technique for the efficient placement and selection of virtual objects that is employed on a 2D panel. We conducted a comparative user study in which our interaction technique outperformed existing approaches regarding precision and task completion time. Furthermore, we demonstrate the effectiveness of the system by a qualitative user study with domain experts.

Nominated for the Best Paper Award.




Quad Layouts via Constrained T-Mesh Quantization


Max Lyon, Marcel Campen, Leif Kobbelt
Eurographics 2021
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We present a robust and fast method for the creation of conforming quad layouts on surfaces. Our algorithm is based on the quantization of a T-mesh, i.e. an assignment of integer lengths to the sides of a non-conforming rectangular partition of the surface. This representation has the benefit of being able to encode an infinite number of layout connectivity options in a finite manner, which guarantees that a valid layout can always be found. We carefully construct the T-mesh from a given seamless parametrization such that the algorithm can provide guarantees on the results' quality. In particular, the user can specify a bound on the angular deviation of layout edges from prescribed directions. We solve an integer linear program (ILP) to find a coarse quad layout adhering to that maximal deviation. Our algorithm is guaranteed to yield a conforming quad layout free of T-junctions together with bounded angle distortion. Our results show that the presented method is fast, reliable, and achieves high quality layouts.




Reducing the Annotation Effort for Video Object Segmentation Datasets


Paul Voigtlaender, Lishu Luo, Chun Yuan, Yong Jiang, Bastian Leibe
2021 Winter Conference on Applications of Computer Vision (WACV ’21)
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For further progress in video object segmentation (VOS), larger, more diverse, and more challenging datasets will be necessary. However, densely labeling every frame with pixel masks does not scale to large datasets. We use a deep convolutional network to automatically create pseudo-labels on a pixel level from much cheaper bounding box annotations and investigate how far such pseudo-labels can carry us for training state-of-the-art VOS approaches. A very encouraging result of our study is that adding a manually annotated mask in only a single video frame for each object is sufficient to generate pseudo-labels which can be used to train a VOS method to reach almost the same performance level as when training with fully segmented videos. We use this workflow to create pixel pseudo-labels for the training set of the challenging tracking dataset TAO, and we manually annotate a subset of the validation set. Together, we obtain the new TAO-VOS benchmark, which we make publicly available at http://www.vision.rwth-aachen.de/page/taovos. While the performance of state-of-the-art methods on existing datasets starts to saturate, TAO-VOS remains very challenging for current algorithms and reveals their shortcomings.

» Show BibTeX

@inproceedings{Voigtlaender21WACV,
title={Reducing the Annotation Effort for Video Object Segmentation Datasets},
author={Paul Voigtlaender and Lishu Luo and Chun Yuan and Yong Jiang and Bastian Leibe},
booktitle={WACV},
year={2021}
}





Poster: Virtual Optical Bench: A VR Learning Tool For Optical Design


Sebastian Pape, Martin Bellgardt, David Gilbert, Georg König, Torsten Wolfgang Kuhlen
IEEE Conference on Virtual Reality and 3D User Interfaces 2021
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The design of optical lens assemblies is a difficult process that requires lots of expertise. The teaching of this process today is done on physical optical benches, which are often too expensive for students to purchase. One way of circumventing these costs is to use software to simulate the optical bench. This work presents a virtual optical bench, which leverages real-time ray tracing in combination with VR rendering to create a teaching tool which creates a repeatable, non-hazardous, and feature-rich learning environment. The resulting application was evaluated in an expert review with 6 optical engineers.




Fast Exact Booleans for Iterated CSG using Octree-Embedded BSPs


Julius Nehring-Wirxel, Philip Trettner, Leif Kobbelt
Computer-Aided Design
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We present octree-embedded BSPs, a volumetric mesh data structure suited for performing a sequence of Boolean operations (iterated CSG) efficiently. At its core, our data structure leverages a plane-based geometry representation and integer arithmetics to guarantee unconditionally robust operations. These typically present considerable performance challenges which we overcome by using custom-tailored fixed-precision operations and an efficient algorithm for cutting a convex mesh against a plane. Consequently, BSP Booleans and mesh extraction are formulated in terms of mesh cutting. The octree is used as a global acceleration structure to keep modifications local and bound the BSP complexity. With our optimizations, we can perform up to 2.5 million mesh-plane cuts per second on a single core, which creates roughly 40-50 million output BSP nodes for CSG. We demonstrate our system in two iterated CSG settings: sweep volumes and a milling simulation.

» Show BibTeX

@article{NEHRINGWIRXEL2021103015,
title = {Fast Exact Booleans for Iterated CSG using Octree-Embedded BSPs},
journal = {Computer-Aided Design},
volume = {135},
pages = {103015},
year = {2021},
issn = {0010-4485},
doi = {https://doi.org/10.1016/j.cad.2021.103015},
url = {https://www.sciencedirect.com/science/article/pii/S0010448521000269},
author = {Julius Nehring-Wirxel and Philip Trettner and Leif Kobbelt},
keywords = {Plane-based geometry, CSG, Mesh Booleans, BSP, Octree, Integer arithmetic},
abstract = {We present octree-embedded BSPs, a volumetric mesh data structure suited for performing a sequence of Boolean operations (iterated CSG) efficiently. At its core, our data structure leverages a plane-based geometry representation and integer arithmetics to guarantee unconditionally robust operations. These typically present considerable performance challenges which we overcome by using custom-tailored fixed-precision operations and an efficient algorithm for cutting a convex mesh against a plane. Consequently, BSP Booleans and mesh extraction are formulated in terms of mesh cutting. The octree is used as a global acceleration structure to keep modifications local and bound the BSP complexity. With our optimizations, we can perform up to 2.5 million mesh-plane cuts per second on a single core, which creates roughly 40-50 million output BSP nodes for CSG. We demonstrate our system in two iterated CSG settings: sweep volumes and a milling simulation.}
}






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