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Mapillary Planet-Scale Depth Dataset 16th

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Mapillary Planet-Scale Depth Dataset

16th European Conference on computer Vision, ECCV 2020

作者： Antequera, Manuel López Gargallo, Pau Hofinger, Markus Bulò, Samuel Rota Kuang, Yubin Kontschieder, Peter Facebook Menlo Park United States Institute of Computer Graphics and Vision Graz University of Technology Graz Austria

ISBN: (纸本)9783030585358

Learning-based methods produce remarkable results on single image depth tasks when trained on well-established benchmarks, however, there is a large gap from these benchmarks to real-world performance that is usually obscured by the common practice of fine-tuning on the target dataset. We introduce a new depth dataset that is an order of magnitude larger than previous datasets, but more importantly, contains an unprecedented gamut of locations, camera models and scene types while offering metric depth (not just up-to-scale). Additionally, we investigate the problem of training single image depth networks using images captured with many different cameras, validating an existing approach and proposing a simpler alternative. With our contributions we achieve excellent results on challenging benchmarks before fine-tuning, and set the state of the art on the popular KITTI dataset after fine-tuning. The dataset is available at ***/dataset/depth. © 2020, Springer Nature Switzerland AG.

关键词： Cameras

Visual Exploration of Cultural Heritage Collections with Linked Spatiotemporal, Shape and Metadata Views

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Visual Exploration of Cultural Heritage Collections with Lin...

2020 Vision, Modeling, and Visualization, VMV 2020

作者： Lengauer, S. Komar, A. Karl, S. Trinkl, E. Preiner, R. Schreck, T. Institute of Computer Graphics and Knowledge Visualisation Graz University of Technology Austria Institute of Classics University of Graz Austria

ISBN: (纸本)9783038681236

The analysis of Cultural Heritage (CH) artefacts is an important task in the Digital Humanities. Increasingly, rich CH artefact data comprising metadata of different modalities becomes available in digital libraries and research data repositories. However, the large amounts and heterogeneity of artefacts in these repositories compromise their accessibility for common domain analysis tasks, as domain researchers lack a structural overview of the spatial, temporal, and categorical traits of the artefacts in these collections. Still, researchers need to compare artefacts along different modalities, put them into context, and deal with possible uncertainties, subjectivities, or missing data. To date, many works support domain research via interactive visualisation. The majority relies primarily on visualisation of text and metadata including spatiotemporal, image and shape data. However, fewer consider these types of data in a tightly coupled way. We present an approach for tightly integrated multimodal visual exploration of large CH data collections along space, time and shape traits. Based on requirements obtained in collaboration with domain researchers, we introduce a set of interlinked views for exploration of said modalities. An appropriately defined approach automatically computes most significant correlations across different modalities, guiding the user towards detecting interesting artefact relationships. We apply our approach to pertinent archaeological data collections, and demonstrate that characteristic explorative tasks are effectively supported and domain-relevant artefact relations can be discovered. © 2020 The Author(s) Eurographics Proceedings © 2020 The Eurographics Association.

关键词： Metadata

A Baseline Solution for the ISBI 2024 Dreaming Challenge

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A Baseline Solution for the ISBI 2024 Dreaming Challenge

IEEE International Symposium on Biomedical Imaging

作者： Timo Van Meegdenburg Gijs Luijten Jens Kleesiek Behrus Puladi André Ferreira Jan Egger Christina Gsaxner Institute for Artificial Intelligence in Medicine University Hospital Essen (AöR) Department of Oral and Maxillofacial Surgery University Hospital Aachen Institute of Medical Informatics University Hospital Aachen Center Algoritmi / LASI University of Minho Braga Portugal Institute of Computer Graphics and Vision Graz University of Technology

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Diminished Reality is a technique for the removal of objects from the surroundings, providing a better view of otherwise obstructed features. This work explores the application of Diminished Reality in a medical setting, specifically aiming to visually eliminate surgical tools from operation sites for improved visibility and inspection. To this end, we introduce the Diminished Reality for Emerging Applications in Medicine through Inpainting (DREAMING) challenge. For the challenge, we created a dataset of simulated surgery scenes with obstructions from surgical instruments and hands. We evaluate an existing video inpainting model to generate diminished images from our dataset to set a baseline for the challenge. We show that the off-the-shelf model already delivers satisfactory results across some conditions, but several limitations prohibit its application for the proposed real-world scenario.

关键词： Training Instruments Video sequences Pipelines Surgery Lighting Data models

Quantum Transport in Open Spin Chains using Neural-Network Quantum States

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arXiv 2022年

作者： Mellak, Johannes Arrigoni, Enrico Pock, Thomas von der Linden, Wolfgang Institute of Theoretical and Computational Physics Graz University of Technology Petersgasse 16/II GrazA-8010 Austria Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II GrazA-8010 Austria

In this work we study the treatment of asymmetric open quantum systems with neural-networks based on the restricted Boltzmann machine. In particular, we are interested in the non-equilibrium steady state current in the boundary-driven (anisotropic) Heisenberg spin chain. We address previously published difficulties in treating asymmetric dissipative systems with neural-network quantum states and Monte-Carlo sampling and present an optimization method and a sampling technique that can be used to obtain high-fidelity steady state approximations of such systems. We point out some inherent symmetries of the Lindblad operator under consideration and exploit them during sampling. We show that local observables are not always a good indicator of the quality of the approximation and finally present results for the spin current that are in agreement with known results of simple open Heisenberg chains. Copyright © 2022, The Authors. All rights reserved.

关键词： Monte Carlo methods

Machine-learned Regularization and Polygonization of Building Segmentation Masks

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Machine-learned Regularization and Polygonization of Buildin...

International Conference on Pattern Recognition

作者： Stefano Zorzi Ksenia Bittner Friedrich Fraundorfer Institute of Computer Graphics and Vision Graz University of Technology Remote Sensing Technology Institute German Aerospace Center (DLR)

ISBN: (纸本)9781728188089;9781728188096

We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building segmentation maps exploiting generic fullyconvolutionalnetwork(FCN). A generativeadversarialnetwork(GAN) is then involved to perform a regularization of building boundaries to make them more realistic, i.e., having more rectilinear outlines which construct right angles if required. This is achieved through the interplay between the discriminator which gives a probability of input image being true and generator that learns from discriminator's response to create more realistic images. Finally, we train the backbone convolutionalneuralnetwork(CNN) which is adapted to predict sparse outcomes corresponding to building corners out of regularized building s

关键词： Geometry Image segmentation Buildings Neural networks Machine learning Generative adversarial networks Generators

Automatically Annotating Indoor Images with CAD Models via RGB-D Scans

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arXiv 2022年

作者： Ainetter, Stefan Stekovic, Sinisa Fraundorfer, Friedrich Lepetit, Vincent Institute for Computer Graphics and Vision Graz University of Technology Graz Austria LIGM École des Ponts Univ Gustave Eiffel CNRS Marne-la-Vallée France

We present an automatic method for annotating images of indoor scenes with the CAD models of the objects by relying on RGB-D scans. Through a visual evaluation by 3D experts, we show that our method retrieves annotations that are at least as accurate as manual annotations, and can thus be used as ground truth without the burden of manually annotating 3D data. We do this using an analysis-by-synthesis approach, which compares renderings of the CAD models with the captured scene. We introduce a'cloning procedure' that identifies objects that have the same geometry, to annotate these objects with the same CAD models. This allows us to obtain complete annotations for the ScanNet dataset and the recent ARKitScenes dataset. Source code and data will be available at https://***/stefan-ainetter/SCANnotate. Copyright © 2022, The Authors. All rights reserved.

关键词： computer aided design

Standard vs. non-standard cross-validation: evaluation of performance in a space with structured distribution of datapoints

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Procedia computer Science 2021年 192卷 1245-1254页

作者： Grzegorz Baron Urszula Stańczyk Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Cross-validation is a popularly used approach to evaluation of performance for classifiers. It relies on random selection of independent samples for training and testing, and assumes that if any similarities among samples exist, they do not lead to known grouping of datapoints in the input space. If these conditions are violated, as it may happen for datasets with some structure of samples included, standard cross-validation can return biased results even for many folds. In the paper the research on cross-validation was reported for application to stylometric datasets, describing a task of authorship attribution. The comparison of standard and non-standard processing was presented. In the latter case, selected subsets of examples were swapped over between training and test sets several times. The experiments with three popular classifiers showed that standard cross-validation tended to give over-optimistic results, whereas non-standard processing was more guarded, and by that more reliable. To avoid high computational costs involved, evaluation based on averaged predictions for limited numbers of test sets can be considered as a reasonable compromise.

关键词： Cross-validation Fold Evaluation of Performance Classification Distribution Stylometry

Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN

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arXiv 2024年

作者： Thaler, Franz Štern, Darko Plank, Gernot Urschler, Martin Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria AVL List GmbH Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria

Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases and consequently, a major cause for mortality and morbidity worldwide. Accurate assessment of myocardial tissue viability for post-MI patients is critical for diagnosis and treatment planning, e.g. allowing surgical revascularization, or to determine the risk of adverse cardiovascular events in the future. Fine-grained analysis of the myocardium and its surrounding anatomical structures can be performed by combining the information obtained from complementary medical imaging techniques. In this work, we use late gadolinium enhanced (LGE) magnetic resonance (MR), T2-weighted (T2) MR and balanced steady-state free precession (bSSFP) cine MR in order to semantically segment the left and right ventricle, healthy and scarred myocardial tissue, as well as edema. To this end, we propose the Multi-Sequence Cascading Refinement CNN (MS-CaRe-CNN), a 2-stage CNN cascade that receives multi-sequence data and generates predictions of the anatomical structures of interest without considering tissue viability at Stage 1. The prediction of Stage 1 is then further refined in Stage 2, where the model additionally distinguishes myocardial tissue based on viability, i.e. healthy, scarred and edema regions. Our proposed method is set up as a 5-fold ensemble and semantically segments scar tissue achieving 62.31% DSC and 82.65% precision, as well as 63.78% DSC and 87.69% precision for the combined scar and edema region. These promising results for such small and challenging structures confirm that MS-CaRe-CNN is well-suited to generate semantic segmentations to assess the viability of myocardial tissue, enabling downstream tasks like personalized therapy planning. © 2024, CC BY-SA.

关键词： Medical imaging

Resize Me! Exploring the User Experience of Embodied Realistic Modulatable Avatars for Body Image Intervention in Virtual Reality

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arXiv 2022年

作者： Döllinger, Nina Wolf, Erik Mal, David Wenninger, Stephan Botsch, Mario Latoschik, Marc Erich Wienrich, Carolin Human-Technology-Systems Group University of Würzburg Würzburg Germany Human-Computer Interaction Group University of Würzburg Würzburg Germany Computer Graphics Group TU Dortmund University Dortmund Germany

Obesity is a serious disease that can affect both physical and psychological well-being. Due to weight stigmatization, many affected individuals suffer from body image disturbances whereby they perceive their body in a distorted way, evaluate it negatively, or neglect it. Beyond established interventions such as mirror exposure, recent advancements aim to complement body image treatments by the embodiment of visually altered virtual bodies in virtual reality (VR). We present a high-fidelity prototype of an advanced VR system that allows users to embody a rapidly generated personalized, photorealistic avatar and to realistically modulate its body weight in real-time within a carefully designed virtual environment. In a formative multi-method approach, a total of 12 participants rated the general user experience (UX) of our system during body scan and VR experience using semi-structured qualitative interviews and multiple quantitative UX measures. By using body weight modification tasks, we further compared three different interaction methods for real-time body weight modification and measured our system’s impact on the body image relevant measures body awareness and body weight perception. From the feedback received, demonstrating an already solid UX of our overall system and providing constructive input for further improvement, we derived a set of design guidelines to guide future development and evaluation processes of systems supporting body image interventions. © 2022, CC BY.

关键词： Virtual reality