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IB-MVS: An iterative algorithm for deep multi-view stereo based on binary decisions

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arXiv 2021年

作者： Sormann, Christian Rossi, Mattia Kuhn, Andreas Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Austria Sony Europe B.V. R&D Center - Stuttgart Laboratory 1 Germany

We present a novel deep-learning-based method for Multi-View Stereo. Our method estimates high resolution and highly precise depth maps iteratively, by traversing the continuous space of feasible depth values at each pixel in a binary decision fashion. The decision process leverages a deep-network architecture: this computes a pixelwise binary mask that establishes whether each pixel actual depth is in front or behind its current iteration individual depth hypothesis. Moreover, in order to handle occluded regions, at each iteration the results from different source images are fused using pixelwise weights estimated by a second network. Thanks to the adopted binary decision strategy, which permits an efficient exploration of the depth space, our method can handle high resolution images without trading resolution and precision. This sets it apart from most alternative learning-based Multi-View Stereo methods, where the explicit discretization of the depth space requires the processing of large cost volumes. We compare our method with state-of-the-art Multi-View Stereo methods on the DTU, Tanks and Temples and the challenging ETH3D benchmarks and show competitive results. Copyright © 2021, The Authors. All rights reserved.

关键词： Iterative methods

DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo

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arXiv 2022年

作者： Sormann, Christian Santellani, Emanuele Rossi, Mattia Kuhn, Andreas Fraundorfer, Friedrich Graz University of Technology Institute of Computer Graphics and Vision Austria Sony Europe B.V. R&D Center Stuttgart Laboratory 1 Germany

We propose a novel approach for deep learning-based Multi-View Stereo (MVS). For each pixel in the reference image, our method leverages a deep architecture to search for the corresponding point in the source image directly along the corresponding epipolar line. We denote our method DELS-MVS: Deep Epipolar Line Search Multi-View Stereo. Previous works in deep MVS select a range of interest within the depth space, discretize it, and sample the epipolar line according to the resulting depth values: this can result in an uneven scanning of the epipolar line, hence of the image space. Instead, our method works directly on the epipolar line: this guarantees an even scanning of the image space and avoids both the need to select a depth range of interest, which is often not known a priori and can vary dramatically from scene to scene, and the need for a suitable discretization of the depth space. In fact, our search is iterative, which avoids the building of a cost volume, costly both to store and to process. Finally, our method performs a robust geometry-aware fusion of the estimated depth maps, leveraging a confidence predicted alongside each depth. We test DELS-MVS on the ETH3D, Tanks and Temples and DTU benchmarks and achieve competitive results with respect to state-of-the-art approaches. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections

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arXiv 2023年

作者： Mirza, M. Jehanzeb Karlinsky, Leonid Lin, Wei Kozinski, Mateusz Possegger, Horst Feris, Rogerio Bischof, Horst Institute of Computer Graphics and Vision TU Graz Austria Christian Doppler Laboratory for Embedded Machine Learning Austria MIT-IBM Watson AI Lab United States

Recently, large-scale pre-trained vision and Language (VL) models have set a new state-of-the-art (SOTA) in zero-shot visual classification enabling open-vocabulary recognition of potentially unlimited set of categories defined as simple language prompts. However, despite these great advances, the performance of these zero-shot classifiers still falls short of the results of dedicated (closed category set) classifiers trained with supervised fine-tuning. In this paper we show, for the first time, how to reduce this gap without any labels and without any paired VL data, using an unlabeled image collection and a set of texts auto-generated using a Large Language Model (LLM) describing the categories of interest and effectively substituting labeled visual instances of those categories. Using our label-free approach, we are able to attain significant performance improvements over the zero-shot performance of the base VL model and other contemporary methods and baselines on a wide variety of datasets, demonstrating absolute improvement of up to 11.7% (3.8% on average) in the label-free setting. Moreover, despite our approach being label-free, we observe 1.3% average gains over leading few-shot prompting baselines that do use 5-shot supervision. © 2023, CC BY.

关键词： Image classification

Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

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arXiv 2023年

作者： Leitner, Stefan Mirza, M. Jehanzeb Lin, Wei Micorek, Jakub Masana, Marc Kozinski, Mateusz Possegger, Horst Bischof, Horst Institute for Computer Graphics and Vision Graz University of Technology Austria Christian Doppler Laboratory for Embedded Machine Learning Austria Christian Doppler Laboratory for Semantic 3D Computer Vision Austria TU Graz SAL Dependable Embedded Systems Lab Silicon Austria Labs Austria

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier. © 2023, CC BY.

关键词： Object detection

Testing cross-platform streaming of video games over wired and wireless LANs

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Testing cross-platform streaming of video games over wired a...

24th IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2010

作者： Jurgelionis, A. Bellotti, F. De Gloria, A. Laulajainen, J.-P. Fechteler, P. Eisert, P. David, H. Department of Biophysical and Electronic Engineering University of Genoa Genoa Italy Converging Networks Laboratory VTT Technical Research Centre of Finland Oulu Finland Computer Vision and Graphics Image Processing Department Heinrich-Hertz-Institute Fraunhofer-Institute for Telecommunications Berlin Germany R and D Department Exent Technologies Ltd. Petach Tikva Israel

ISBN: (纸本)9780769540191

In this paper we present a new cross-platform approach for video game delivery in wired and wireless local networks. The developed 3D streaming and video streaming approaches enable users to access video games on set top boxes and handheld devices that natively are not capable to run PC games. During the development of the distributed gaming system we have faced a number of challenges and problems posed by the hardware and network limitations. In order to solve these problems we have developed a multilevel testing methodology which is based on user assessment and technical measures for the system under development. In this paper we focus on the technical measures and instrumentation that we use for the system's performance measurement and testing. The benefits of our testing methodology are demonstrated through examples from the development and testing work. © 2010 IEEE.

关键词： Human computer interaction

vision-based Unmanned Aerial Vehicle detection and tracking for sense and avoid systems

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Vision-based Unmanned Aerial Vehicle detection and tracking ...

IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Krishna Raj Sapkota Steven Roelofsen Artem Rozantsev Vincent Lepetit Denis Gillet Pascal Fua Alcherio Martinoli Computer Vision Laboratory School of Communication and Computer Sciences école Polytechnique Fédérale de Lausanne (EPFL) Switzerland Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering EPFL Switzerland Institute for Computer Graphics and Vision TU Graz Austria Coordination and Interaction System Group School of Engineering EPFL Switzerland

ISBN: (纸本)9781509037636

We propose an approach for on-line detection of small Unmanned Aerial Vehicles (UAVs) and estimation of their relative positions and velocities in the 3D environment from a single moving camera in the context of sense and avoid systems. This problem is challenging both from a detection point of view, as there are no markers on the targets available, and from a tracking perspective, due to misdetection and false positives. Furthermore, the methods need to be computationally light, despite the complexity of computer vision algorithms, to be used on UAVs with limited payload. To address these issues we propose a multi-staged framework that incorporates fast object detection using an AdaBoost-based approach, coupled with an on-line visual-based tracking algorithm and a recent sensor fusion and state estimation method. Our framework allows for achieving real-time performance with accurate object detection and tracking without any need of markers and customized, high-performing hardware resources.

关键词： Target tracking Object detection Collision avoidance Cameras Visualization Detectors

EC-SfM: Efficient Covisibility-based Structure-from-Motion for Both Sequential and Unordered Images

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arXiv 2023年

作者： Ye, Zhichao Bao, Chong Zhou, Xin Liu, Haomin Bao, Hujun Zhang, Guofeng State Key Laboratory of Computer Aided Design and Computer Graphics Zhejiang University Hangzhou310058 China SenseTime Research Hangzhou311215 China ZJU-SenseTime Joint Lab of 3D Vision China

Structure-from-Motion is a technology used to obtain scene structure through image collection, which is a fundamental problem in computer vision. For unordered Internet images, SfM is very slow due to the lack of prior knowledge about image overlap. For sequential images, knowing the large overlap between adjacent frames, SfM can adopt a variety of acceleration strategies, which are only applicable to sequential data. To further improve the reconstruction efficiency and break the gap of strategies between these two kinds of data, this paper presents an efficient covisibility-based incremental SfM. Different from previous methods, we exploit covisibility and registration dependency to describe the image connection which is suitable to any kind of data. Based on this general image connection, we propose a unified framework to efficiently reconstruct sequential images, unordered images, and the mixture of these two. Experiments on the unordered images and mixed data verify the effectiveness of the proposed method, which is three times faster than the state-of-the-art on feature matching, and an order of magnitude faster on reconstruction without sacrificing the accuracy. The source code is publicly available at https://***/openxrlab/xrsfm. © 2023, CC BY.

关键词： HTTP

Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data

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arXiv 2019年

作者： Gsaxner, Christina Roth, Peter M. Wallner, Jürgen Egger, Jan Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz Styria Austria

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

An IoT Course Program to Foster the Adoption of IoT Driven Food and Agriculture in Sub-Saharan Africa (SSA)

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An IoT Course Program to Foster the Adoption of IoT Driven F...

2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Kuaban, Godlove Suila Nowak, Mateusz Czekalski, Piotr Tokarz, Krzysztof Tangka, Julius Kewir Siggursson, Kjartan Nikitenko, Agris Berkolds, Karlis Sell, Raivo Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice 44-100 Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Renewable Energy Laboratory University of Dschang Dschang Cameroon University of Twente Netherlands Riga Technical University Latvia TalTech Estonia

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

The Internet of Things (IoT) technologies, together with other technological advances such as big data analytics, Artificial Intelligence (AI), automation, and unmanned electrical vehicles (e.g., self-driving vehicles and drones), are currently being used to transform the food and agriculture industry to increase yields significantly with minimal damage to the environment. These recent advances in Information and Communication Technologies and advances in biotechnology are driving the transition into the fourth agricultural revolution or agriculture 4.0. Some research studies on the adoption of IoT in the agriculture sector in Sub-Saharan Africa concluded that some of the reasons the rate of adoption of IoT in Africa is still very low are gaps in knowledge, skills, finance, and infrastructure. The knowledge and skills gaps are partly a result of an educational curriculum that is not up to date with current advances in the food and agricultural industry. To foster IoT adoption in the food and agriculture sectors and other sectors or industries in Sub-saharan Africa, we propose an IoT course program consisting of lectures and laboratory modules. The course is accompanied by textbooks, video lectures that are freely available on Edx, and remote online laboratories with the hardware resources hosted in some European countries but accessible through the Internet. These courses can also be adapted by vocational training schools, Higher Institutes, and universities in Sub-Saharan Africa or could be used by professionals and enthusiasts to improve their skills. © 2022 IEEE.

关键词： Agriculture