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Applying Double Skin Facade with ETFE Membrane Assembly for Energy Saving and Acoustic Protection for the Building of the czech institute of informatics,robotics and cybernetics in Prague

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Journal of Civil Engineering and Architecture 2019年第3期13卷 178-185页

作者： Petr Franta Architects&Assoc. Ltd.PragueCzech Republic Czech Institute of Informatics Robotics and Cybernetics-Czech Technical UniversityPragueCzech Republic

Multidisciplinary, integrated planning approach by architects, engineers, scientists and manufacturers to reduce energy consumption of buildings. The CIIRC Complex, located on the main campus of czech Technical University in Prague consists of two buildings, newly constructed building and adaptive reuse of existing building. CIIRC—czech institute of informatics, robotics and cybernetics is a contemporary teaching facility of new generation and use for scientific research teams. New building has ten above-ground floors, on the bottom 4 floors of laboratories, scientist modules, classrooms, above are offices, meeting rooms, teaching and research modules for professors and students. Offices of the rector are on the last two floors of the building. On the top floor is congress type auditorium, in the basement is fully automatic car park. Double skin pneumatic cushions facade. In the project are introduced series of architectural and technical features and innovations. Probably the most visible is the double skin facade facing south-transparent double layer membrane ETFE (Ethylen-TetraFluorEthylen) cushions with triple glazed modular system assembly. Acting as solar collector, recuperating of hot air on the top floors, saving up to 30% of an energy consumption.

关键词： Double skin façade as solar collector ETFE membrane cushions as outer skin air-recuperation from façade(top floors).

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Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

Comparative Evaluation of 3D Reconstruction Methods for Obje...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Burde, Varun Benbihi, Assia Burget, Pavel Sattler, Torsten Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic

ISBN: (纸本)9798331510831

Current generalizable object pose estimators, i.e., approaches that do not need to be trained per object, rely on accurate 3D models. Predominantly, CAD models are used, which can be hard to obtain in practice. At the same time, it is often possible to acquire images of an object. Naturally, this leads to the question of whether 3D models reconstructed from images are sufficient to facilitate accurate object pose estimation. We aim to answer this question by proposing a novel benchmark for measuring the impact of 3D reconstruction quality on pose estimation accuracy. Our benchmark provides calibrated images suitable for reconstruction and registered with the test images of the YCB-V dataset for pose evaluation under the BOP benchmark format. Detailed experiments with multiple state-of-the-art 3D reconstruction and object pose estimation approaches show that the geometry produced by modern reconstruction methods is often sufficient for accurate pose estimation. Our experiments lead to interesting observations: (1) Standard metrics for measuring 3D reconstruction quality are not necessarily indicative of pose estimation accuracy, which shows the need for dedicated benchmarks such as ours. (2) Classical, non-learning-based approaches can perform on par with modern learning-based reconstruction techniques and can even offer a better reconstruction time-pose accuracy tradeoff. (3) There is still a sizable gap between performance with reconstructed and with CAD models. To foster research on closing this gap, the benchmark is made available at https://***/VarunBurde/reconstruction-pose-benchmark. © 2025 IEEE.

关键词： 3d reconstruction 6d object pose estimation bop

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Wearable Sensors and Computational Intelligence in Alpine Skiing Analysis

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IEEE Access 2025年 13卷 70414-70421页

作者： Procházka, Aleš Charvátová, Hana University of Chemistry and Technology in Prague Department of Mathematics Informatics and Cybernetics Prague160 00 Czech Republic Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Prague160 00 Czech Republic Tomas Bata University in Zlín Faculty of Applied Informatics Zlín760 01 Czech Republic

The integration of wearable sensors with artificial intelligence forms the base for analyzing physical activities through digital signal processing, numerical methods, and machine learning. Computational intelligence and communication technologies enable personalized monitoring, training, and rehabilitation, with applications in sports, neurology, and biomedicine. This paper focuses on motion analysis in alpine skiing using real accelerometric, gyroscopic, positioning, and video data to evaluate ski movement patterns. The proposed methodology employs functional transforms to estimate motion patterns and utilizes artificial intelligence for signal segmentation and feature classification related to lower limb movement. Machine learning results indicate differences in energy distribution before and after ski turns and demonstrate the feasibility of classifying associated motion patterns with accuracies of 98.1% and 90.7%, respectively, using a two-layer neural network. The interdisciplinary application of computational intelligence in this domain enhances motion analysis, injury prevention, and performance optimization. This study highlights the unifying role of digital signal processing, which uses similar mathematical tools across various applications. © 2013 IEEE.

关键词： Gyroscopes

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EdgeGaussians - 3D Edge Mapping via Gaussian Splatting

EdgeGaussians - 3D Edge Mapping via Gaussian Splatting

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Chelani, Kunal Benbihi, Assia Sattler, Torsten Kahl, Fredrik Chalmers University of Technology Sweden Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic

ISBN: (纸本)9798331510831

With their meaningful geometry and omnipresence in the 3D world, edges are extremely useful primitives in computer vision. Methods for 3D edge reconstruction have 1) either focused on reconstructing 3D edges by triangulating tracks of 2D line segments across images or 2) more recently, learning a 3D edge distance field from multi-view images. The triangulation-based methods struggle to repeatedly detect and robustly match line segments resulting in noisy and incomplete reconstructions in many cases. Methods in the latter class rely on sampling edge points from the learnt implicit field, which is limited by the spatial resolution of the voxel grid used for sampling, resulting in imprecise points that require refinement. Further, such methods require a long training that scales poorly with the size of the scene. In this paper, we propose a method that explicitly learns 3D edge points with a 3D Gaussian Splatting representation trained from edge images. The 3D Gaussians are regularized to have their directions of largest variance along the edge they lie on, enabling clustering into separate edges. Backed by efficient training, the proposed method produces results better than or at-par with the current state-of-the-art methods, while being an order of magnitude faster. Code released at https://***/kunalchelani/EdgeGaussians. © 2025 IEEE.

关键词： Triangulation

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FocalPose++: Focal Length and Object Pose Estimation via Render and Compare

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第2期47卷 755-772页

作者： Cifka, Martin Ponimatkin, Georgy Labbe, Yann Russell, Bryan Aubry, Mathieu Petrik, Vladimir Sivic, Josef Czech Technical University in Prague Faculty of Electrical Engineering Czech Institute of Informatics Robotics and Cybernetics Prague16000 Czech Republic Meta Platforms Reality Labs Zürich8045 Switzerland Adobe Research San FranciscoCA94103 United States École des Ponts LIGM UGE CNRS Marne-la-Vallée77455 France Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Prague16000 Czech Republic

We introduce FocalPose++, a neural render-and-compare method for jointly estimating the camera-object 6D pose and camera focal length given a single RGB input image depicting a known object. The contributions of this work are threefold. First, we derive a focal length update rule that extends an existing state-of-the-art render-and-compare 6D pose estimator to address the joint estimation task. Second, we investigate several different loss functions for jointly estimating the object pose and focal length. We find that a combination of direct focal length regression with a reprojection loss disentangling the contribution of translation, rotation, and focal length leads to improved results. Third, we explore the effect of different synthetic training data on the performance of our method. Specifically, we investigate different distributions used for sampling object's 6D pose and camera's focal length when rendering the synthetic images, and show that parametric distribution fitted on real training data works the best. We show results on three challenging benchmark datasets that depict known 3D models in uncontrolled settings. We demonstrate that our focal length and 6D pose estimates have lower error than the existing state-of-the-art methods. © 1979-2012 IEEE.

关键词： Digital elevation model

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Comparative Evaluation of 3D Reconstruction Methods for Object Pose Estimation

Comparative Evaluation of 3D Reconstruction Methods for Obje...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Varun Burde Assia Benbihi Pavel Burget Torsten Sattler Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

关键词： Geometry Solid modeling Three-dimensional displays Accuracy Pose estimation Benchmark testing Reconstruction algorithms Rendering (computer graphics) Image reconstruction Standards

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TransforMerger: Transformer-based Voice-Gesture Fusion for Robust Human-Robot Communication

arXiv

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arXiv 2025年

作者： Vanc, Petr Stepanova, Karla Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Republic

As human-robot collaboration advances, natural and flexible communication methods are essential for effective robot control. Traditional methods relying on a single modality or rigid rules struggle with noisy or misaligned data as well as with object descriptions that do not perfectly fit the predefined object names (e.g.’Pick that red object’). We introduce TransforMerger, a transformer-based reasoning model that infers a structured action command for robotic manipulation based on fused voice and gesture inputs. Our approach merges multimodal data into a single unified sentence, which is then processed by the language model. We employ probabilistic embeddings to handle uncertainty and we integrate contextual scene understanding to resolve ambiguous references (e.g., gestures pointing to multiple objects or vague verbal cues like"this"). We evaluate TransforMerger in simulated and real-world experiments, demonstrating its robustness to noise, misalignment, and missing information. Our results show that TransforMerger outperforms deterministic baselines, especially in scenarios requiring more contextual knowledge, enabling more robust and flexible human-robot communication. Code and datasets are available at: http://***. ***/publications/transformerger. © 2025, CC BY-NC-SA.

关键词： Gesture recognition

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EdgeGaussians - 3D Edge Mapping via Gaussian Splatting

EdgeGaussians - 3D Edge Mapping via Gaussian Splatting

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Kunal Chelani Assia Benbihi Torsten Sattler Fredrik Kahl Chalmers University of Technology Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

关键词： Training Geometry Image segmentation Computer vision Three-dimensional displays Image edge detection Rendering (computer graphics) Noise measurement Spatial resolution Image reconstruction

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Embedded Hierarchical MPC for Autonomous Navigation

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IEEE Transactions on robotics 2025年

作者： Benders, Dennis Kohler, Johannes Niesten, Thijs Babuska, Robert Alonso-Mora, Javier Ferranti, Laura Delft University of Technology department of Cognitive Robotics CD Delft2628 Netherlands ETH Zürich Institute for Dynamic Systems and Control CH 8092 Switzerland Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Republic

To efficiently deploy robotic systems in society, mobile robots must move autonomously and safely through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby obstacles. However, the limited computation power available on typical embedded robotic systems, such as quadrotors, poses a challenge to running MPC in real time, including its most expensive tasks: constraints generation and optimization. To address this problem, we propose a novel hierarchical MPC scheme that consists of a planning and a tracking layer. The planner constructs a trajectory with a long prediction horizon at a slow rate, while the tracker ensures trajectory tracking at a relatively fast rate. We prove that the proposed framework avoids collisions and is recursively feasible. Furthermore, we demonstrate its effectiveness in simulations and lab experiments with a quadrotor that needs to reach a goal position in a complex static environment. The code is efficiently implemented on the quadrotor's embedded computer to ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC formulation, this allows us to increase the planning horizon by a factor of 5, which results in significantly better performance. © 2004-2012 IEEE.

关键词： Mobile robots

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Towards Resilient and Sustainable Global Industrial Systems: An Evolutionary-Based Approach

arXiv

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arXiv 2025年

作者： Jirkovský, Václav Kubalík, Jiří Kadera, Petr Schirrmann, Arnd Mitschke, Andreas Zindel, Andreas Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague16000 Czech Republic Airbus Central R&T Germany

This paper presents a new complex optimization problem in the field of automatic design of advanced industrial systems and proposes a hybrid optimization approach to solve the problem. The problem is multi-objective as it aims at finding solutions that minimize CO2 emissions, transportation time, and costs. The optimization approach combines an evolutionary algorithm and classical mathematical programming to design resilient and sustainable global manufacturing networks. Further, it makes use of the OWL ontology for data consistency and constraint management. The experimental validation demonstrates the effectiveness of the approach in both single and double sourcing scenarios. The proposed methodology, in general, can be applied to any industry case with complex manufacturing and supply chain challenges. © 2025, CC BY.

关键词： Smart manufacturing

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