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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Thomas Kernbauer Maximilian Tschulik Philipp Fleck Clemens Arth Institute of Computer Graphics and Vision Graz University of Technology

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

Operating heavy machinery is challenging and can pose safety hazards for the operator and bystanders. Although commonly used augmented reality (AR) devices, such as head-mounted or head-up displays, can provide occupational support to operators, they can also cause problems. Particularly in off-highway scenarios, i.e., when driving machines in bumpy environments, the usefulness of current AR devices and the willingness of operators to wear them are limited. Therefore, we explore how laser-projection-based AR can help the operator facilitate their tasks and enhance safety. For this, we present a compact hardware unit and introduce a flexible and declarative software system. Furthermore, we examine the calibration process to leverage a camera projector setup and outline a process for creating images suitable for display by a laser projector from a set of line segments. Finally, we showcase its ability to provide efficient instructions to operators and bystanders and propose concrete applications for our setup.

关键词： Solid modeling Three-dimensional displays User interfaces Distortion Cameras Software systems Hardware

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Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed

Efficient Motion Prediction: A Lightweight & Accurate Trajec...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Alexander Prutsch Horst Bischof Horst Possegger Institute of Computer Graphics and Vision Graz University of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

For efficient and safe autonomous driving, it is essential that autonomous vehicles can predict the motion of other traffic agents. While highly accurate, current motion prediction models often impose significant challenges in terms of training resource requirements and deployment on embedded hardware. We propose a new efficient motion prediction model, which achieves highly competitive benchmark results while training only a few hours on a single GPU. Due to our lightweight architectural choices and the focus on reducing the required training resources, our model can easily be applied to custom datasets. Furthermore, its low inference latency makes it particularly suitable for deployment in autonomous applications with limited computing resources.

关键词： Training Accuracy Computational modeling graphics processing units computer architecture Predictive models Transformers Trajectory Autonomous vehicles Standards

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Action-By-Detection: Efficient Forklift Action Detection for Autonomous Mobile Robots in Warehouses

Action-By-Detection: Efficient Forklift Action Detection for...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alexander Prutsch Horst Possegger Horst Bischof Institute of Computer Graphics and Vision Graz University of Technology

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Understanding actions of other agents increases the efficiency of autonomous mobile robots (AMRs) since they encompass intention and indicate future movements. We propose a new method that allows us to infer vehicle actions using a shallow image-based classification model. The actions are classified via bird’s-eye view scene crops, where we project the detections of a 3D object detection model onto a context map. We learn map context information and aggregate temporal sequence information without requiring object tracking. This results in a highly efficient classification model that can easily be deployed on embedded AMR hardware. To evaluate our approach, we create new large-scale synthetic datasets showing warehouse traffic based on real vehicle models and geometry.

关键词： Point cloud compression Solid modeling Three-dimensional displays Pipelines Detectors Predictive models Mobile robots

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HAMMER: Learning Entropy Maps to Create Accurate 3D Models in Multi-View Stereo

HAMMER: Learning Entropy Maps to Create Accurate 3D Models i...

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IEEE Workshop on Applications of computer vision (WACV)

作者： Rafael Weilharter Friedrich Fraundorfer Institute of Computer Graphics and Vision Graz University of Technology

While the majority of recent Multi-View Stereo Networks estimates a depth map per reference image, their performance is then only evaluated on the fused 3D model obtained from all images. This approach makes a lot of sense since ultimately the point cloud is the result we are mostly interested in. On the flip side, it often leads to a burdensome manual search for the right fusion parameters in order to score well on the public benchmarks. In this work, we tackle the aforementioned problem with HAMMER, a Hierarchical And Memory-efficient MVSNet with Entropy-filtered Reconstructions. We propose to learn a filtering mask based on entropy, which, in combination with a simple two-view geometric verification, is sufficient to generate high quality 3D models of any input scene. Distinct from existing works, a tedious manual parameter search for the fusion step is not required. Furthermore, we take several precautions to keep the memory requirements for our method very low in the training as well as in the inference phase. Our method only requires 6 GB of GPU memory during training, while 3.6 GB are enough to process 1920×1024 images during inference. Experiments show that HAMMER ranks amongst the top published methods on the DTU and Tanks and Temples benchmarks in the official metrics, especially when keeping the fusion parameters fixed.

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Transparency Distortion Robustness for SOTA Image Segmentation Tasks 4th

Transparency Distortion Robustness for SOTA Image Segmentat...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Knauthe, Volker Rak, Arne Wirth, Tristan Pöllabauer, Thomas Metzler, Simon Kuijper, Arjan Fellner, Dieter W. Technical University of Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany CGV Institute Graz University of Technology Graz Austria

ISBN: (纸本)9789819787043

Semantic Image Segmentation facilitates a multitude of real-world applications ranging from autonomous driving over industrial process supervision to vision aids for human beings. These models are usually trained in a supervised fashion using example inputs. Distribution Shifts between these examples and the inputs in operation may cause erroneous segmentations. The robustness of semantic segmentation models against distribution shifts caused by differing camera or lighting setups, lens distortions, adversarial inputs and image corruptions has been topic of recent research. However, robustness against spatially varying radial distortion effects that can be caused by uneven glass structures (e.g. windows) or the chaotic refraction in heated air has not been addressed by the research community yet. We propose a method to synthetically augment existing datasets with spatially varying distortions. Our experiments show, that these distortion effects degrade the performance of state-of-the-art segmentation models. Pretraining and enlarged model capacities proof to be suitable strategies for mitigating performance degradation to some degree, while fine-tuning on distorted images only leads to marginal performance improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantic Segmentation

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Influence of Water Droplet Contamination for Transparency Segmentation 4th

Influence of Water Droplet Contamination for Transparency ...

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4th International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2024

作者： Knauthe, Volker Weitz, Paul Pöllabauer, Thomas Wirth, Tristan Rak, Arne Kuijper, Arjan Fellner, Dieter W. Technical University of Darmstadt Darmstadt Germany Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany CGV Institute Graz University of Technology Graz Austria

ISBN: (纸本)9789819787043

computer vision techniques are on the rise for industrial applications, like process supervision and autonomous agents, e.g., in the healthcare domain and dangerous environments. While the general usability of these techniques is high, there are still challenging real-world use-cases. Especially transparent structures, which can appear in the form of glass doors, protective casings or everyday objects like glasses, pose a challenge for computer vision methods. This paper evaluates the combination of transparent objects in conjunction with (naturally occurring) contamination through environmental effects like hazing. We introduce a novel publicly available dataset containing 489 images incorporating three grades of water droplet contamination on transparent structures and examine the resulting influence on transparency handling. Our findings show, that contaminated transparent objects are easier to segment and that we are able to distinguish between different severity levels of contamination with a current state-of-the art machine-learning model. This in turn opens up the possibility to enhance computer vision systems regarding resilience against, e.g., datashifts through contaminated protection casings or implement an automated cleaning alert. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Into the Fog: Evaluating Robustness of Multiple Object Tracking

arXiv

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arXiv 2024年

作者： Kirillova, Nadezda Mirza, M. Jehanzeb Bischof, Horst Possegger, Horst Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

State-of-the-art Multiple Object Tracking (MOT) approaches have shown remarkable performance when trained and evaluated on current benchmarks. However, these benchmarks primarily consist of clear weather scenarios, overlooking adverse atmospheric conditions such as fog, haze, smoke and dust. As a result, the robustness of trackers against these challenging conditions remains underexplored. To address this gap, we introduce physics-based volumetric fog simulation method for arbitrary MOT datasets, utilizing frame-by-frame monocular depth estimation and a fog formation optical model. We enhance our simulation by rendering both homogeneous and heterogeneous fog and propose to use the dark channel prior method to estimate atmospheric light, showing promising results even in night and indoor scenes. We present the leading benchmark MOTChallenge (third release) augmented with fog (smoke for indoor scenes) of various intensities and conduct a comprehensive evaluation of MOT methods, revealing their limitations under fog and fog-like challenges. © 2024, CC BY.

关键词： Object tracking

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Efficient Motion Prediction: A Lightweight & Accurate Trajectory Prediction Model With Fast Training and Inference Speed

arXiv

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arXiv 2024年

作者： Prutsch, Alexander Bischof, Horst Possegger, Horst The Institute of Computer Graphics and Vision Graz University of Technology Austria

关键词： Prediction models

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Brain computer Interfacing with a Virtual Environment 32

Brain Computer Interfacing with a Virtual Environment

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32nd International Conference in Central Europe on computer graphics, Visualization and computer vision, WSCG 2024

作者： Gamillscheg, Florian Ruprecht, Irena Settgast, Volker Pietroszek, Krzysztof Augsdörfer, Ursula Institute of Computer Graphics and Knowledge Visualisation Graz University of Technology Austria Fraunhofer Austria Graz Austria American University WashingtonDC United States Graz University of Technology Austria

Virtual Reality (VR) applications constantly strive for more realism, immersion and intuitive user experiences. Traditional VR controllers can hinder full immersion, since they form an additional barrier between the user’s thoughts or intentions and the virtual world. Brain computer interfaces (BCIs) have the potential to close this gap by enabling an immediate translation of human thoughts to commands that can be processed by a computer. This paper investigates the feasibility of employing an affordable commercial BCI device for VR interaction. In a preliminary study conducted in a Cave Automatic Virtual Environment (CAVE), we evaluate both the effectiveness and limitations of the popular BCI device Emotiv Insight. © 2024 university of West Bohemia. All rights reserved.

关键词： Virtual environments

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TAEC: Unsupervised action segmentation with temporal-Aware embedding and clustering 26

TAEC: Unsupervised action segmentation with temporal-Aware e...

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26th computer vision Winter Workshop, CVWW 2023

作者： Lin, Wei Kukleva, Anna Possegger, Horst Kuehne, Hilde Bischof, Horst Institute of Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Austria Max-Planck-Institute for Informatics Germany Goethe University Frankfurt Germany

Temporal action segmentation in untrimmed videos has gained increased attention recently. However, annotating action classes and frame-wise boundaries is extremely time consuming and cost intensive, especially on large-scale datasets. To address this issue, we propose an unsupervised approach for learning action classes from untrimmed video sequences. In particular, we propose a temporal embedding network that combines relative time prediction, feature reconstruction, and sequence-To-sequence learning, to preserve the spatial layout and sequential nature of the video features. A two-step clustering pipeline on these embedded feature representations then allows us to enforce temporal consistency within, as well as across videos. Based on the identified clusters, we decode the video into coherent temporal segments that correspond to semantically meaningful action classes. Our evaluation on three challenging datasets shows the impact of each component and, furthermore, demonstrates our state-of-The-Art unsupervised action segmentation results. © 2023 Copyright for this paper by its authors.

关键词： Large dataset

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