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Publications


 

From Points to Multi-Object 3D Reconstruction


Francis Engelmann, Konstantinos Rematas, Bastian Leibe, Vittorio Ferrari
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021
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We propose a method to detect and reconstruct multiple 3D objects from a single RGB image. The key idea is to optimize for detection, alignment and shape jointly over all objects in the RGB image, while focusing on realistic and physically plausible reconstructions. To this end, we propose a keypoint detector that localizes objects as center points and directly predicts all object properties, including 9-DoF bounding boxes and 3D shapes -- all in a single forward pass. The proposed method formulates 3D shape reconstruction as a shape selection problem, i.e. it selects among exemplar shapes from a given database. This makes it agnostic to shape representations, which enables a lightweight reconstruction of realistic and visually-pleasing shapes based on CAD-models, while the training objective is formulated around point clouds and voxel representations. A collision-loss promotes non-intersecting objects, further increasing the reconstruction realism. Given the RGB image, the presented approach performs lightweight reconstruction in a single-stage, it is real-time capable, fully differentiable and end-to-end trainable. Our experiments compare multiple approaches for 9-DoF bounding box estimation, evaluate the novel shape-selection mechanism and compare to recent methods in terms of 3D bounding box estimation and 3D shape reconstruction quality.

» Show BibTeX

@inproceedings{Engelmann21CVPR,
title = {{From Points to Multi-Object 3D Reconstruction}},
author = {Engelmann, Francis and Rematas, Konstantinos and Leibe, Bastian and Ferrari, Vittorio},
booktitle = {{IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}},
year = {2021}
}





3D Shape Generation with Grid-based Implicit Functions


Moritz Ibing, Isaak Lim, Leif Kobbelt
IEEE Conference on Computer Vision and Pattern Recognition
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Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE). Even though this produces convincing results, it has two major shortcomings. As the GAN is limited to reproduce the dataset the AE was trained on, we cannot reuse a trained AE for novel data. Furthermore, it is difficult to add spatial supervision into the generation process, as the AE only gives us a global representation. To remedy these issues, we propose to train the GAN on grids (i.e. each cell covers a part of a shape). In this representation each cell is equipped with a latent vector provided by an AE. This localized representation enables more expressiveness (since the cell-based latent vectors can be combined in novel ways) as well as spatial control of the generation process (e.g. via bounding boxes). Our method outperforms the current state of the art on all established evaluation measures, proposed for quantitatively evaluating the generative capabilities of GANs. We show limitations of these measures and propose the adaptation of a robust criterion from statistical analysis as an alternative.

» Show BibTeX

@inproceedings {ibing20213Dshape,
title = {3D Shape Generation with Grid-based Implicit Functions},
author = {Ibing, Moritz and Lim, Isaak and Kobbelt, Leif},
booktitle = {IEEE Computer Vision and Pattern Recognition (CVPR)},
pages = {},
year = {2021}
}





Implicit Density Projection for Volume Conserving Liquids


Tassilo Kugelstadt, Andreas Longva, Nils Thuerey, Jan Bender
IEEE Transactions on Visualization and Computer Graphics
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We propose a novel implicit density projection approach for hybrid Eulerian/Lagrangian methods like FLIP and APIC to enforce volume conservation of incompressible liquids. Our approach is able to robustly recover from highly degenerate configurations and incorporates volume-conserving boundary handling. A problem of the standard divergence-free pressure solver is that it only has a differential view on density changes. Numerical volume errors, which occur due to large time steps and the limited accuracy of pressure projections, are invisible to the solver and cannot be corrected. Moreover, these errors accumulate over time and can lead to drastic volume changes, especially in long-running simulations or interactive scenarios. Therefore, we introduce a novel method that enforces constant density throughout the fluid. The density itself is tracked via the particles of the hybrid Eulerian/Lagrangian simulation algorithm. To achieve constant density, we use the continuous mass conservation law to derive a pressure Poisson equation which also takes density deviations into account. It can be discretized with standard approaches and easily implemented into existing code by extending the regular pressure solver. Our method enables us to relax the strict time step and solver accuracy requirements of a regular solver, leading to significantly higher performance. Moreover, our approach is able to push fluid particles out of solid obstacles without losing volume and generates more uniform particle distributions, which makes frequent particle resampling unnecessary. We compare the proposed method to standard FLIP and APIC and to previous volume correction approaches in several simulations and demonstrate significant improvements in terms of incompressibility, visual realism and computational performance.

» Show BibTeX

@Article{BKKW21,
author = {Tassilo Kugelstadt and Andreas Longva and Nils Thuerey and Jan Bender},
title = {Implicit Density Projection for Volume Conserving Liquids},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
publisher = {IEEE},
volume = {27},
number = {4},
doi={ 10.1109/TVCG.2019.2947437},
}





to be presented: Being Guided or Having Exploratory Freedom: User Preferences of a Virtual Agent’s Behavior in a Museum


Andrea Bönsch, David Hashem, Jonathan Ehret, Torsten Wolfgang Kuhlen
21th ACM International Conference on Intelligent Virtual Agents 2021 (IVA'21)
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A virtual guide in an immersive virtual environment allows users a structured experience without missing critical information. However, although being in an interactive medium, the user is only a passive listener, while the embodied conversational agent (ECA) fulfills the active roles of wayfinding and conveying knowledge. Thus, we investigated for the use case of a virtual museum, whether users prefer a virtual guide or a free exploration accompanied by an ECA who imparts the same information compared to the guide. Results of a small within-subjects study with a head-mounted display are given and discussed, resulting in the idea of combining benefits of both conditions for a higher user acceptance. Furthermore, the study indicated the feasibility of the carefully designed scene and ECA’s appearance.



Learning Direction Fields for Quad Mesh Generation


Alexander Dielen, Isaak Lim, Max Lyon, Leif Kobbelt
Eurographics Symposium on Geometry Processing 2021
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State of the art quadrangulation methods are able to reliably and robustly convert triangle meshes into quad meshes. Most of these methods rely on a dense direction field that is used to align a parametrization from which a quad mesh can be extracted. In this context, the aforementioned direction field is of particular importance, as it plays a key role in determining the structure of the generated quad mesh. If there are no user-provided directions available, the direction field is usually interpolated from a subset of principal curvature directions. To this end, a number of heuristics that aim to identify significant surface regions have been proposed. Unfortunately, the resulting fields often fail to capture the structure found in meshes created by human experts. This is due to the fact that experienced designers can leverage their domain knowledge in order to optimize a mesh for a specific application. In the context of physics simulation, for example, a designer might prefer an alignment and local refinement that facilitates a more accurate numerical simulation. Similarly, a character artist may prefer an alignment that makes the resulting mesh easier to animate. Crucially, this higher level domain knowledge cannot be easily extracted from local curvature information alone. Motivated by this issue, we propose a data-driven approach to the computation of direction fields that allows us to mimic the structure found in existing meshes, which could originate from human experts or other sources. More specifically, we make use of a neural network that aggregates global and local shape information in order to compute a direction field that can be used to guide a parametrization-based quad meshing method. Our approach is a first step towards addressing this challenging problem with a fully automatic learning-based method. We show that compared to classical techniques our data-driven approach combined with a robust model-driven method, is able to produce results that more closely exhibit the ground truth structure of a synthetic dataset (i.e. a manually designed quad mesh template fitted to a variety of human body types in a set of different poses).

» Show BibTeX

@article{dielen2021learning_direction_fields,
title={Learning Direction Fields for Quad Mesh Generation},
author={Dielen, Alexander and Lim, Isaak and Lyon, Max and Kobbelt, Leif},
year={2021},
journal={Computer Graphics Forum},
volume={40},
number={5},
}





Simpler Quad Layouts using Relaxed Singularities


Max Lyon, Marcel Campen, Leif Kobbelt
Eurographics Symposium on Geometry Processing 2021
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A common approach to automatic quad layout generation on surfaces is to, in a first stage, decide on the positioning of irregular layout vertices, followed by finding sensible layout edges connecting these vertices and partitioning the surface into quadrilateral patches in a second stage. While this two-step approach reduces the problem's complexity, this separation also limits the result quality. In the worst case, the set of layout vertices fixed in the first stage without consideration of the second may not even permit a valid quad layout. We propose an algorithm for the creation of quad layouts in which the initial layout vertices can be adjusted in the second stage. Whenever beneficial for layout quality or even validity, these vertices may be moved within a prescribed radius or even be removed. Our algorithm is based on a robust quantization strategy, turning a continuous T-mesh structure into a discrete layout. We show the effectiveness of our algorithm on a variety of inputs.

» Show BibTeX

@article{lyon2021simplerlayouts,
title={Simpler Quad Layouts using Relaxed Singularities},
author={Lyon, Max and Campen, Marcel and Kobbelt, Leif},
year={2021},
journal={Computer Graphics Forum},
volume={40},
number={5},
}





Surface Map Homology Inference


Janis Born, Patrick Schmidt, Marcel Campen, Leif Kobbelt
Eurographics Symposium on Geometry Processing 2021
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A homeomorphism between two surfaces not only defines a (continuous and bijective) geometric correspondence of points but also (by implication) an identification of topological features, i.e. handles and tunnels, and how the map twists around them. However, in practice, surface maps are often encoded via sparse correspondences or fuzzy representations that merely approximate a homeomorphism and are therefore inherently ambiguous about map topology. In this work, we show a way to infer topological information from an imperfect input map between two shapes. In particular, we compute a homology map, a linear map that transports homology classes of cycles from one surface to the other, subject to a global consistency constraint. Our inference robustly handles imperfect (e.g., partial, sparse, fuzzy, noisy, outlier-ridden, non-injective) input maps and is guaranteed to produce homology maps that are compatible with true homeomorphisms between the input shapes. Homology maps inferred by our method can be directly used to transfer homological information between shapes, or serve as foundation for the construction of a proper homeomorphism guided by the input map, e.g., via compatible surface decomposition.



This work has received the best paper award at SGP 2021.

» Show BibTeX

@article{born2021surface,
title={Surface Map Homology Inference},
author={Born, Janis and Schmidt, Patrick and Campen, Marcel and Kobbelt, Leif},
year={2021},
journal={Computer Graphics Forum},
volume={40},
number={5},
}





Geodesic Distance Computation via Virtual Source Propagation


Philip Trettner, David Bommes, Leif Kobbelt
Eurographics Symposium on Geometry Processing 2021
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We present a highly practical, efficient, and versatile approach for computing approximate geodesic distances. The method is designed to operate on triangle meshes and a set of point sources on the surface. We also show extensions for all kinds of geometric input including inconsistent triangle soups and point clouds, as well as other source types, such as lines. The algorithm is based on the propagation of virtual sources and hence easy to implement. We extensively evaluate our method on about 10000 meshes taken from the Thingi10k and the Tet Meshing in the Wild data sets. Our approach clearly outperforms previous approximate methods in terms of runtime efficiency and accuracy. Through careful implementation and cache optimization, we achieve runtimes comparable to other elementary mesh operations (e.g. smoothing, curvature estimation) such that geodesic distances become a "first-class citizen" in the toolbox of geometric operations. Our method can be parallelized and we observe up to 6× speed-up on the CPU and 20× on the GPU. We present a number of mesh processing tasks easily implemented on the basis of fast geodesic distances. The source code of our method will be provided as a C++ library under the MIT license.

Note: we are currently in the process of cleaning up and documenting the source code. A basic implementation can already be found in the supplemental material.




Sampling from Quadric-Based CSG Surfaces


Philip Trettner, Leif Kobbelt
High-Performance Graphics 2021
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We present an efficient method to create samples directly on surfaces defined by constructive solid geometry (CSG) trees or graphs. The generated samples can be used for visualization or as an approximation to the actual surface with strong guarantees. We chose to use quadric surfaces as CSG primitives as they can model classical primitives such as planes, cubes, spheres, cylinders, and ellipsoids, but also certain saddle surfaces. More importantly, they are closed under affine transformations, a desirable property for a modeling system. We also propose a rendering method that performs local quadric ray-tracing and clipping to achieve pixel-perfect accuracy and hole-free rendering.




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.



This work has received honorable mentions for the Günter Enderle Best Paper Award and best full paper talk at Eurographics 2021.

» 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}
}





Compression and Rendering of Textured Point Clouds via Sparse Coding


Kersten Schuster, Philip Trettner, Patric Schmitz, Julian Schakib, Leif Kobbelt
High-Performance Graphics 2021
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Splat-based rendering techniques produce highly realistic renderings from 3D scan data without prior mesh generation. Mapping high-resolution photographs to the splat primitives enables detailed reproduction of surface appearance. However, in many cases these massive datasets do not fit into GPU memory. In this paper, we present a compression and rendering method that is designed for large textured point cloud datasets. Our goal is to achieve compression ratios that outperform generic texture compression algorithms, while still retaining the ability to efficiently render without prior decompression. To achieve this, we resample the input textures by projecting them onto the splats and create a fixed-size representation that can be approximated by a sparse dictionary coding scheme. Each splat has a variable number of codeword indices and associated weights, which define the final texture as a linear combination during rendering. For further reduction of the memory footprint, we compress geometric attributes by careful clustering and quantization of local neighborhoods. Our approach reduces the memory requirements of textured point clouds by one order of magnitude, while retaining the possibility to efficiently render the compressed data.




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.

» Show BibTeX

@article{Lyon:2021:Quad,
title = {Quad Layouts via Constrained T-Mesh Quantization},
author = {Lyon, Max and Campen, Marcel and Kobbelt, Leif},
journal = {Computer Graphics Forum},
volume = {40},
number = {2},
year = {2021}
}





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.

» Show BibTeX

@INPROCEEDINGS{Pape2021,
author = {Pape, Sebastian and Bellgardt, Martin and Gilbert, David and König, Georg and Kuhlen, Torsten W.},
booktitle = {2021 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW)},
title = {Virtual Optical Bench: A VR learning tool for optical design},
year = {2021},
volume ={},
number = {},
pages = {635-636},
doi = {10.1109/VRW52623.2021.00200}
}





Domain and Modality Gaps for LiDAR-based Person Detection on Mobile Robots


Dan Jia, Alexander Hermans, Bastian Leibe
arXiv preprint
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Person detection is a crucial task for mobile robots navigating in human-populated environments and LiDAR sensors are promising for this task, given their accurate depth measurements and large field of view. This paper studies existing LiDAR-based person detectors with a particular focus on mobile robot scenarios (e.g. service robot or social robot), where persons are observed more frequently and in much closer ranges, compared to the driving scenarios. We conduct a series of experiments, using the recently released JackRabbot dataset and the state-of-the-art detectors based on 3D or 2D LiDAR sensors (CenterPoint and DR-SPAAM respectively). These experiments revolve around the domain gap between driving and mobile robot scenarios, as well as the modality gap between 3D and 2D LiDAR sensors. For the domain gap, we aim to understand if detectors pretrained on driving datasets can achieve good performance on the mobile robot scenarios, for which there are currently no trained models readily available. For the modality gap, we compare detectors that use 3D or 2D LiDAR, from various aspects, including performance, runtime, localization accuracy, robustness to range and crowdedness. The results from our experiments provide practical insights into LiDAR-based person detection and facilitate informed decisions for relevant mobile robot designs and applications.




Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud


Dan Jia, Bastian Leibe
Accepted as an extended abstract in JRDB-ACT Workshop at CVPR21

In this preliminary work we attempt to apply submanifold sparse convolution to the task of 3D person detection. In particular, we present Person-MinkUNet, a single-stage 3D person detection network based on Minkowski Engine with U-Net architecture. The network achieves a 76.4% average precision (AP) on the JRDB 3D detection benchmark.

Winner of JRDB 3D detection challenge at CVPR 2021




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.}
}





Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera


Dan Jia, Mats Steinweg, Alexander Hermans, Bastian Leibe
IEEE International Conference on Robotics and Automation (ICRA), 2021
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Deep learning is the essential building block of state-of-the-art person detectors in 2D range data. However, only a few annotated datasets are available for training and testing these deep networks, potentially limiting their performance when deployed in new environments or with different LiDAR models. We propose a method, which uses bounding boxes from an image-based detector (e.g. Faster R-CNN) on a calibrated camera to automatically generate training labels (called pseudo-labels) for 2D LiDAR-based person detectors. Through experiments on the JackRabbot dataset with two detector models, DROW3 and DR-SPAAM, we show that self- supervised detectors, trained or fine-tuned with pseudo-labels, outperform detectors trained using manual annotations from a different dataset. Combined with robust training techniques, the self-supervised detectors reach a performance close to the ones trained using manual annotations. Our method is an effective way to improve person detectors during deployment without any additional labeling effort, and we release our source code to support relevant robotic applications.





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