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MULTI-SCALE RAFT: COMBINING HIERARCHICAL CONCEPTS FOR LEARNING-BASED OPTICAL FLOW ESTIMATION

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arXiv 2022年

作者： Jahedi, Azin Mehl, Lukas Rivinius, Marc Bruhn, Andrés Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute of Information Security University of Stuttgart Germany

Many classical and learning-based optical flow methods rely on hierarchical concepts to improve both accuracy and robustness. However, one of the currently most successful approaches - RAFT - hardly exploits such concepts. In this work, we show that multi-scale ideas are still valuable. More precisely, using RAFT as a baseline, we propose a novel multi-scale neural network that combines several hierarchical concepts within a single estimation framework. These concepts include (i) a partially shared coarse-to-fine architecture, (ii) multi-scale features, (iii) a hierarchical cost volume and (iv) a multi-scale multi-iteration loss. Experiments on MPI Sintel and KITTI clearly demonstrate the benefits of our approach. They show not only substantial improvements compared to RAFT, but also state-of-the-art results - in particular in non-occluded regions. Code will be available at https://***/cv-stuttgart/MS_RAFT. Copyright © 2022, The Authors. All rights reserved.

关键词： Optical flows

High-Resolution Multi-Scale RAFT (Robust Vision Challenge 2022)

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arXiv 2022年

作者： Jahedi, Azin Luz, Maximilian Mehl, Lukas Rivinius, Marc Bruhn, Andrés Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute of Information Security University of Stuttgart Germany

In this report, we present our optical flow approach, MS-RAFT+, that won the Robust Vision Challenge 2022. It is based on the MS-RAFT method, which successfully integrates several multi-scale concepts into single-scale RAFT. Our approach extends this method by exploiting an additional finer scale for estimating the flow, which is made feasible by on-demand cost computation. This way, it can not only operate at half the original resolution, but also use MS-RAFT's shared convex upsampler to obtain full resolution flow. Moreover, our approach relies on an adjusted fine-tuning scheme during training. This in turn aims at improving the generalization across benchmarks. Among all participating methods in the Robust Vision Challenge, our approach ranks first on VIPER and second on KITTI, Sintel, and Middlebury, resulting in the first place of the overall ranking. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

ELSKE: Efficient large-scale keyphrase extraction

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arXiv 2021年

作者： Knittel, Johannes Koch, Steffen Ertl, Thomas Institute for Visualization and Interactive Systems University of Stuttgart Germany

Keyphrase extraction methods can provide insights into large collections of documents such as social media posts. Existing methods, however, are less suited for the real-time analysis of streaming data, because they are computationally too expensive or require restrictive constraints regarding the structure of keyphrases. We propose an efficient approach to extract keyphrases from large document collections and show that the method also performs competitively on individual documents. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements

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arXiv 2024年

作者： Charaja, Jhon P.F. Wochner, Isabell Schumacher, Pierre Ilg, Winfried Giese, Martin Maufroy, Christophe Bulling, Andreas Schmitt, Syn Martius, Georg Haeufle, Daniel F.B. Hertie Institute for Clinical Brain Research Centre for Integrative Neuroscience University of Tübingen Germany Heidelberg University Germany Max Planck Institute for Intelligent Systems Tübingen Germany University of Stuttgart Germany Germany Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany

Mimicking of human-like arm movement characteristics involves considering three factors during control policy synthesis: (a) task requirements, (b) noise during movement execution, and (c) optimality principles. Previous studies showed that when these factors (a-c) are considered individually, it is possible to synthesize arm movements that either kinematically match experimental data or reproduce the stereotypical triphasic muscle activation pattern. However, no quantitative comparison has assessed the realism of arm movements generated by each factor, nor has it been determined whether combining these factors results in movements with human-like kinematic characteristics and the triphasic muscle pattern. To investigate this, we used reinforcement learning to learn a control policy for a musculoskeletal arm model, aiming to discern which combination of factors (a-c) results in realistic arm movements according to four frequently reported stereotypical characteristics. Our findings indicate that incorporating velocity and acceleration requirements into the reaching task, employing reward terms that minimize mechanical work, hand jerk, and control effort, along with the inclusion of noise during movement, leads to realistic human arm movements by reinforcement learning. We expect that the gained insights will help in the future to better predict desired arm movements and corrective forces in wearable assistive devices. Copyright © 2024, The Authors. All rights reserved.

关键词： Kinematics

Touching the Moon: Leveraging Passive Haptics, Embodiment and Presence for Operational Assessments in Virtual Reality

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arXiv 2024年

作者： Dufresne, Florian Nilsson, Tommy Gorisse, Geoffrey Guerra, Enrico Zenner, André Christmann, Olivier Bensch, Leonie Callus, Nikolai Anton Cowley, Aidan Arts et Métiers Institute of Technology Changé France European Space Agency Cologne Germany University Duisburg-Essen Duisburg Germany Saarland University DFKI Saarbrücken Germany Institute for Software Technology Software for Space Systems and Interactive Visualization Cologne Germany

Space agencies are in the process of drawing up carefully thought-out Concepts of Operations (ConOps) for future human missions on the Moon. These are typically assessed and validated through costly and logistically demanding analogue field studies. While interactive simulations in Virtual Reality (VR) offer a comparatively cost-effective alternative, they have faced criticism for lacking the fidelity of real-world deployments. This paper explores the applicability of passive haptic interfaces in bridging the gap between simulated and real-world ConOps assessments. Leveraging passive haptic props (equipment mockup and astronaut gloves), we virtually recreated the Apollo 12 mission procedure and assessed it with experienced astronauts and other space experts. Quantitative and qualitative findings indicate that haptics increased presence and embodiment, thus improving perceived simulation fidelity and validity of user reflections. We conclude by discussing the potential role of passive haptic modalities in facilitating early-stage ConOps assessments for human endeavours on the Moon and *** Codes 93B51, 97M50 Copyright © 2024, The Authors. All rights reserved.

关键词： Virtual reality

Quantifying Human Upper Limb Stiffness Responses Based on a Computationally Efficient Neuromusculoskeletal Arm Model

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Quantifying Human Upper Limb Stiffness Responses Based on a ...

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Maria Sapounaki Pierre Schumacher Winfried Ilg Martin Giese Christophe Maufroy Andreas Bulling Syn Schmitt Daniel F.B. Haeufle Isabell Wochner Institute of Computer Engineering (ZITI) Heidelberg University Germany Hertie Institute for Clinical Brain Research and Werner Reichard Centre for Integrative Neuroscience University of Tübingen Tübingen Germany Institut for Industrial Manufacturing and Management (IFF) University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) Germany Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Activities of daily living such as drinking and eating can be severely impaired for patients suffering from neurodegenerative diseases. One promising solution are assistive devices that apply corrective forces while still allowing the intended movements. However, real-time estimation of the required forces requires a detailed understanding of the limb's impedance characteristics. Here, we test and validate the stiffness response of a computationally efficient neuro-musculoskeletal arm model and its response to various force perturbations. We demonstrate that the arm model predicts stiffness characteristics that closely match experimental data recorded from humans and presents real-time applicability, allowing for implementation in practical scenarios and. Additionally, we predict the stiffness response for novel force levels and arm configurations. In the future, these predictions could be used to estimate corrective forces for assistive devices in real-time.

关键词： Computational modeling Perturbation methods Force Estimation Predictive models Manipulators Real-time systems Computational efficiency Assistive devices Impedance

Generating Realistic Arm Movements in Reinforcement Learning: A Quantitative Comparison of Reward Terms and Task Requirements

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Generating Realistic Arm Movements in Reinforcement Learning...

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Jhon P.F. Charaja Isabell Wochner Pierre Schumacher Winfried Ilg Martin Giese Christophe Maufroy Andreas Bulling Syn Schmitt Georg Martius Daniel F.B. Haeufle Hertie Institute for Clinical Brain Research and Centre for Integrative Neuroscience University of Tübingen Germany Institute of Computer Engineering (ZITI) Heidelberg University Germany Max Planck Institute for Intelligent Systems Tübingen Germany Institut for Industrial Manufacturing and Management (IFF) University of Stuttgart Germany Fraunhofer Institute for Manufacturing Engineering and Automation (IPA) Germany Institute for Visualization and Interactive Systems University of Stuttgart Germany Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Department of Computer Science University of Tübingen Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

关键词： Noise Reinforcement learning Kinematics Biomechatronics Muscles Manipulators Assistive devices Pattern matching

MultiAR: A Multi-User Augmented Reality Platform for Biomedical Education

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MultiAR: A Multi-User Augmented Reality Platform for Biomedi...

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Markus Perz Gijs Luijten Jens Kleesiek Dieter Schmalstieg Jan Egger Christina Gsaxner Institute of Computer Graphics and Vision Graz University of Technology Graz Austria Institute for Artificial Intelligence in Medicine University Medicine Essen Essen Germany Cancer Research Center Cologne Essen West German Cancer Center Essen Germany Institute for Visualization and Interactive Systems University of Stuttgart Stuttgart Germany Virtual and Extended Reality in Medicine (ZvRM) University Hospital Essen Essen Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper addresses the growing integration of Augmented Reality (AR) in biomedical sciences, emphasizing collaborative learning experiences. We present MultiAR, a versatile, domain-specific platform enabling multi-user interactions in AR for biomedical education. Unlike platform-specific solutions, MultiAR supports various AR devices, including handheld and head-mounted options. The framework extends across domains, augmenting biomedical education applications with collaborative capabilities. We define essential requirements for a multi-user AR framework in education, detail MultiAR’s design and implementation, and comprehensively evaluate it using anatomy education examples. Quantitative and qualitative analyses, covering system performance, accuracy metrics, and a user study with 20 participants, highlight the urgent need for a tailored collaborative AR platform in biomedical education. Results underscore enthusiasm for collaborative AR technology, endorsing MultiAR as an accessible, versatile solution for developers and end-users in biomedical education.

关键词： Measurement Accuracy Federated learning System performance Education Collaboration Anatomy Engineering in medicine and biology Augmented reality

Directional priors for multi-frame optical flow 29

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Directional priors for multi-frame optical flow

29th British Machine Vision Conference, BMVC 2018

作者： Maurer, Daniel Stoll, Michael Bruhn, Andres Institute for Visualization and Interactive Systems University of Stuttgart Germany

Pipeline approaches that interpolate and refine an initial set of point correspondences have recently shown a good performance in the field of optical flow estimation. However, so far, these methods are typically restricted to two frames which makes exploiting temporal information difficult. In this paper, we show how such pipeline approaches can be extended to the temporal domain and how directional constraints can be incorporated to further improve the estimation. To this end, we not only suggest to exploit temporal information in the prefiltering step, we also propose a trajectorial refinement method that lifts successful concepts of recent variational two-frame methods to the multi-frame domain. Experiments demonstrate the usefulness of our pipeline approach. They do not only show good results in general, they also demonstrate the clear benefits of using multiple frames and of imposing directional constraints on the prefiltering step and the refinement. © 2018. The copyright of this document resides with its authors.

关键词： Optical flows