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28th International Conference on Knowledge Based and Intelligent information and Engineering Systems, KES 2024

作者： Stanczyk, Urszula Baron, Grzegorz Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

The paper presents research dedicated to observations of relations between attribute properties and discretisation. In the investigations described, the gradually increasing sets of features were discretised by selected approaches, and several variants of data were constructed. The continuous, partially discrete, and completely translated datasets were explored by the chosen classifiers and their performance studied in the context of a number of discretised attributes, discretisation procedures, and the way of processing of features and datasets. The stylometric problem of authorship attribution was the machine learning task under study. The experimental results enable to observe closer the specificity of style-markers employed as characteristic features, and indicate conditions for efficient recognition of authorship. They can be extended to other application domains with similar characteristics. © 2024 The Authors.

关键词： Data assimilation

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BUILDING HEIGHT ESTIMATION USING SHADOW LENGTH IN SATELLITE IMAGERY

arXiv

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arXiv 2024年

作者： Chaudhry, Shayaan Qureshi, Mahd Jabbar, Sana Taj, Murtaza Computer Vision and Graphics Lab LUMS Pakistan

Estimating building height from satellite imagery poses significant challenges, especially when monocular images are employed, resulting in a loss of essential 3D information during imaging. This loss of spatial depth further complicates the height estimation process. We addressed this issue by using shadow length as an additional cue to compensate for the loss of building height estimation using single-view imagery. We proposed a novel method that first localized a building and its shadow in the given satellite image. After localization, the shadow length is estimated using a regression model. To estimate the final height of each building, we utilize the principles of photogrammetry, specifically considering the relationship between the solar elevation angle, the vertical edge length of the building, and the length of the building’s shadow. For the localization of buildings in our model, we utilized a modified YOLOv7 detector, and to regress the shadow length for each building we utilized the ResNet18 as backbone architecture. Finally, we estimated the associated building height using solar elevation with shadow length through analytical formulation. We evaluated our method on 42 different cities and the results showed that the proposed framework surpasses the state-of-the-art methods with a suitable margin. Our code is available at: https://***/nullptr-code/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Satellite imagery

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CAMERA CALIBRATION THROUGH GEOMETRIC CONSTRAINTS FROM ROTATION AND PROJECTION MATRICES

arXiv

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arXiv 2024年

作者： Waleed, Muhammad Rauf, Abdul Taj, Murtaza Computer Vision and Graphics Lab LUMS Pakistan

The process of camera calibration involves estimating the intrinsic and extrinsic parameters, which are essential for accurately performing tasks such as 3D reconstruction, object tracking and augmented reality. In this work, we propose a novel constraints-based loss for measuring the intrinsic (focal length: (fx, fy) and principal point: (px, py)) and extrinsic (baseline: (b), disparity: (d), translation: (tx, ty, tz), and rotation specifically pitch: (θp)) camera parameters. Our novel constraints are based on geometric properties inherent in the camera model, including the anatomy of the projection matrix (vanishing points, image of world origin, axis planes) and the orthonormality of the rotation matrix. Thus we proposed a novel Unsupervised Geometric Constraint Loss (UGCL) via a multitask learning framework. Our methodology is a hybrid approach that employs the learning power of a neural network to estimate the desired parameters along with the underlying mathematical properties inherent in the camera projection matrix. This distinctive approach not only enhances the interpretability of the model but also facilitates a more informed learning process. Additionally, we introduce a new CVGL Camera Calibration dataset, featuring over 900 configurations of camera parameters, incorporating 63, 600 image pairs that closely mirror real-world conditions. By training and testing on both synthetic and real-world datasets, our proposed approach demonstrates improvements across all parameters when compared to the state-of-the-art (SOTA) benchmarks. The code and the updated dataset can be found here: https://***/CVLABLUMS/CVGL-Camera-Calibration. © 2024, CC BY-NC-ND.

关键词： Augmented reality

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Spatial Augmented Reality for Heavy Machinery Using Laser Projections

Spatial Augmented Reality for Heavy Machinery Using Laser Pr...

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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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DNN-based Skeleton Independent Motion Retargeting 27

DNN-based Skeleton Independent Motion Retargeting

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Akber, Syed Muhammad Abrar Kazmi, Sadia Nishat Szczesna, Agnieszka Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

In the modern digital world, the use of animated applications has increased significantly and such applications have quietly become an integral part of life. Capturing human motions is a fundamental aspect of these applications which is performed by motion-capturing (MoCap) systems. Due to the heterogeneity of MoCap systems and marker configurations, the captured skeletons may vary in their structure, number of joints, and bone lengths. The need for motion retargeting arises to transfer motion captured from one articulated actor to another. Although motion retargeting is a long-standing issue, current methods cannot automatically execute retargeting between skeletons. This paper presents the retraining of a popular motion retargeting network with a new large-scale motion dataset. The retraining aims to demonstrate the capabilities of the motion retargeting network to handle the problem of motion retargeting between skeletons that may have different structures. The paper first, presents the motion retargeting network that employs an encoder-decoder architecture. Afterward, we re-train the motion retargeting network with a new MoCap dataset. To validate the effectiveness of the network, we visually compare the generated results with the existing retargeting model. The results show that the motion retargeting network effectively retargets the different motions. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Decoder Deep neural network Encoder Motion retargeting

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SLAM Methods for Augmented Reality Systems for Flight Simulators 23rd

SLAM Methods for Augmented Reality Systems for Flight Simu...

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23rd International Conference on Computational Science, ICCS 2023

作者： Nwobodo, Onyeka J. Wereszczyński, Kamil Cyran, Krzysztof Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

ISBN: (纸本)9783031359941

In this paper, we present the review and practical evaluation of the flight simulators of Simultaneous Localization and Mapping methods. We present a review of recent research and development in the SLAM application in a wide range of domains, like autonomous driving, robotics and augmented reality (AR). Then we focus on the methods selected from the perspective of their usefulness in the AR systems for training and servicing the flight simulators. The localization and mapping in such an environment are much more complex than in others since the flight simulator is relatively small and close area. Our previous experiments showed that the built-in SLAM system in HoloLens is insufficient for such areas and has to be enhanced with additional elements, like QR codes. Therefore, the presented study on other methods can improve the localization and mapping of AR systems in flight simulators. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Augmented reality

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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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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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A review on tracking head movement in augmented reality systems 27

A review on tracking head movement in augmented reality syst...

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27th International Conference on Knowledge Based and Intelligent Information and Engineering Sytems, KES 2023

作者： Nwobodo, Onyeka J. Wereszczynski, Kamil Cyran, Krzysztof Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland

This paper reviews the recent development of augmented reality (AR) head-tracking techniques. AR is an emerging technology that provides users with digital augmentation layered on the real-life environment. AR systems use tracking to maintain the point of reference and enable the device to follow the user's movements and position within the physical world. Head-tracking is a crucial component of AR that inputs and interprets the movement of the device and the user's physical location in real-time. The main challenge in head-tracking is the appearance change that results from head rotation, which necessitates high accuracy and long-range tracking in a noisy environment. Latency and errors in head-tracking can reduce the system's overall effectiveness and cause jarring or unsettling effects for the user. This paper's contribution is to shed light on the best methods for tracking and registering in augmented reality, which will guide future advancements in the field. The review covers recent work proposed to improve head-tracking performance and reduce latency, highlighting the challenges and future directions of head-tracking techniques in AR. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： augmented reality deep learning DOF Head tracking immersive experience pose estimation

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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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