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  1. Article ; Online: Dense Continuous-Time Optical Flow from Event Cameras.

    Gehrig, Mathias / Muglikar, Manasi / Scaramuzza, Davide

    IEEE transactions on pattern analysis and machine intelligence

    2024  Volume PP

    Abstract: We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel ... ...

    Abstract We present a method for estimating dense continuous-time optical flow from event data. Traditional dense optical flow methods compute the pixel displacement between two images. Due to missing information, these approaches cannot recover the pixel trajectories in the blind time between two images. In this work, we show that it is possible to compute per-pixel, continuous-time optical flow using events from an event camera. Events provide temporally fine-grained information about movement in pixel space due to their asynchronous nature and microsecond response time. We leverage these benefits to predict pixel trajectories densely in continuous time via parameterized Bézier curves. To achieve this, we build a neural network with strong inductive biases for this task: First, we build multiple sequential correlation volumes in time using event data. Second, we use Bézier curves to index these correlation volumes at multiple timestamps along the trajectory. Third, we use the retrieved correlation to update the Bézier curve representations iteratively. Our method can optionally include image pairs to boost performance further. To the best of our knowledge, our model is the first method that can regress dense pixel trajectories from event data. To train and evaluate our model, we introduce a synthetic dataset (MultiFlow) that features moving objects and ground truth trajectories for every pixel. Our quantitative experiments not only suggest that our method successfully predicts pixel trajectories in continuous time but also that it is competitive in the traditional two-view pixel displacement metric on MultiFlow and DSEC-Flow. Open source code and datasets are released to the public.
    Language English
    Publishing date 2024-02-02
    Publishing country United States
    Document type Journal Article
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2024.3361671
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Cracking double-blind review: Authorship attribution with deep learning.

    Bauersfeld, Leonard / Romero, Angel / Muglikar, Manasi / Scaramuzza, Davide

    PloS one

    2023  Volume 18, Issue 6, Page(s) e0287611

    Abstract: Double-blind peer review is considered a pillar of academic research because it is perceived to ensure a fair, unbiased, and fact-centered scientific discussion. Yet, experienced researchers can often correctly guess from which research group an ... ...

    Abstract Double-blind peer review is considered a pillar of academic research because it is perceived to ensure a fair, unbiased, and fact-centered scientific discussion. Yet, experienced researchers can often correctly guess from which research group an anonymous submission originates, biasing the peer-review process. In this work, we present a transformer-based, neural-network architecture that only uses the text content and the author names in the bibliography to attribute an anonymous manuscript to an author. To train and evaluate our method, we created the largest authorship-identification dataset to date. It leverages all research papers publicly available on arXiv amounting to over 2 million manuscripts. In arXiv-subsets with up to 2,000 different authors, our method achieves an unprecedented authorship attribution accuracy, where up to 73% of papers are attributed correctly. We present a scaling analysis to highlight the applicability of the proposed method to even larger datasets when sufficient compute capabilities are more widely available to the academic community. Furthermore, we analyze the attribution accuracy in settings where the goal is to identify all authors of an anonymous manuscript. Thanks to our method, we are not only able to predict the author of an anonymous work but we also provide empirical evidence of the key aspects that make a paper attributable. We have open-sourced the necessary tools to reproduce our experiments.
    MeSH term(s) Authorship ; Deep Learning ; Double-Blind Method ; Electric Power Supplies ; Neural Networks, Computer
    Language English
    Publishing date 2023-06-30
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0287611
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Book ; Online: Learning Agile, Vision-based Drone Flight

    Scaramuzza, Davide / Kaufmann, Elia

    from Simulation to Reality

    2023  

    Abstract: We present our latest research in learning deep sensorimotor policies for agile, vision-based quadrotor flight. We show methodologies for the successful transfer of such policies from simulation to the real world. In addition, we discuss the open ... ...

    Abstract We present our latest research in learning deep sensorimotor policies for agile, vision-based quadrotor flight. We show methodologies for the successful transfer of such policies from simulation to the real world. In addition, we discuss the open research questions that still need to be answered to improve the agility and robustness of autonomous drones toward human-pilot performance.
    Keywords Computer Science - Robotics
    Publishing date 2023-04-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Flymation

    Song, Yunlong / Scaramuzza, Davide

    Interactive Animation for Flying Robots

    2023  

    Abstract: Trajectory visualization and animation play critical roles in robotics research. However, existing data visualization and animation tools often lack flexibility, scalability, and versatility, resulting in limited capability to fully explore and analyze ... ...

    Abstract Trajectory visualization and animation play critical roles in robotics research. However, existing data visualization and animation tools often lack flexibility, scalability, and versatility, resulting in limited capability to fully explore and analyze flight data. To address this limitation, we introduce Flymation, a new flight trajectory visualization and animation tool. Built on the Unity3D engine, Flymation is an intuitive and interactive tool that allows users to visualize and analyze flight data in real time. Users can import data from various sources, including flight simulators and real-world data, and create customized visualizations with high-quality rendering. With Flymation, users can choose between trajectory snapshot and animation; both provide valuable insights into the behavior of the underlying autonomous system. Flymation represents an exciting step toward visualizing and interacting with large-scale data in robotics research.

    Comment: This work was presented at Workshop at ICRA 2023 ( The Role of Robotics Simulators for Unmanned Aerial Vehicles)
    Keywords Computer Science - Robotics
    Subject code 629
    Publishing date 2023-10-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Editorial: Radiomics and AI for clinical and translational medicine.

    Montin, Eros / Corino, Valentina D A / Martel, Dimitri / Carlucci, Giuseppe / Scaramuzza, Davide

    Frontiers in radiology

    2024  Volume 4, Page(s) 1375443

    Language English
    Publishing date 2024-02-19
    Publishing country Switzerland
    Document type Editorial
    ISSN 2673-8740
    ISSN (online) 2673-8740
    DOI 10.3389/fradi.2024.1375443
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Reaching the limit in autonomous racing: Optimal control versus reinforcement learning.

    Song, Yunlong / Romero, Angel / Müller, Matthias / Koltun, Vladlen / Scaramuzza, Davide

    Science robotics

    2023  Volume 8, Issue 82, Page(s) eadg1462

    Abstract: A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with ...

    Abstract A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.
    Language English
    Publishing date 2023-09-13
    Publishing country United States
    Document type Journal Article
    ISSN 2470-9476
    ISSN (online) 2470-9476
    DOI 10.1126/scirobotics.adg1462
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: Visual attention prediction improves performance of autonomous drone racing agents.

    Pfeiffer, Christian / Wengeler, Simon / Loquercio, Antonio / Scaramuzza, Davide

    PloS one

    2022  Volume 17, Issue 3, Page(s) e0264471

    Abstract: Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select task-relevant visual information effectively. This work investigates whether neural networks capable of ... ...

    Abstract Humans race drones faster than neural networks trained for end-to-end autonomous flight. This may be related to the ability of human pilots to select task-relevant visual information effectively. This work investigates whether neural networks capable of imitating human eye gaze behavior and attention can improve neural networks' performance for the challenging task of vision-based autonomous drone racing. We hypothesize that gaze-based attention prediction can be an efficient mechanism for visual information selection and decision making in a simulator-based drone racing task. We test this hypothesis using eye gaze and flight trajectory data from 18 human drone pilots to train a visual attention prediction model. We then use this visual attention prediction model to train an end-to-end controller for vision-based autonomous drone racing using imitation learning. We compare the drone racing performance of the attention-prediction controller to those using raw image inputs and image-based abstractions (i.e., feature tracks). Comparing success rates for completing a challenging race track by autonomous flight, our results show that the attention-prediction based controller (88% success rate) outperforms the RGB-image (61% success rate) and feature-tracks (55% success rate) controller baselines. Furthermore, visual attention-prediction and feature-track based models showed better generalization performance than image-based models when evaluated on hold-out reference trajectories. Our results demonstrate that human visual attention prediction improves the performance of autonomous vision-based drone racing agents and provides an essential step towards vision-based, fast, and agile autonomous flight that eventually can reach and even exceed human performances.
    MeSH term(s) Fixation, Ocular ; Humans ; Neural Networks, Computer ; Unmanned Aerial Devices ; Vision, Ocular
    Language English
    Publishing date 2022-03-01
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0264471
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Online: Recurrent Vision Transformers for Object Detection with Event Cameras

    Gehrig, Mathias / Scaramuzza, Davide

    2022  

    Abstract: We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These ...

    Abstract We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object detection and tracking in time-critical scenarios. Prior work in event-based vision has achieved outstanding detection performance but at the cost of substantial inference time, typically beyond 40 milliseconds. By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance. To achieve this, we explore a multi-stage design that utilizes three key concepts in each stage: First, a convolutional prior that can be regarded as a conditional positional embedding. Second, local and dilated global self-attention for spatial feature interaction. Third, recurrent temporal feature aggregation to minimize latency while retaining temporal information. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection - achieving an mAP of 47.2% on the Gen1 automotive dataset. At the same time, RVTs offer fast inference (<12 ms on a T4 GPU) and favorable parameter efficiency (5 times fewer than prior art). Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 006 ; 004
    Publishing date 2022-12-11
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Are High-Resolution Event Cameras Really Needed?

    Gehrig, Daniel / Scaramuzza, Davide

    2022  

    Abstract: Due to their outstanding properties in challenging conditions, event cameras have become indispensable in a wide range of applications, ranging from automotive, computational photography, and SLAM. However, as further improvements are made to the sensor ... ...

    Abstract Due to their outstanding properties in challenging conditions, event cameras have become indispensable in a wide range of applications, ranging from automotive, computational photography, and SLAM. However, as further improvements are made to the sensor design, modern event cameras are trending toward higher and higher sensor resolutions, which result in higher bandwidth and computational requirements on downstream tasks. Despite this trend, the benefits of using high-resolution event cameras to solve standard computer vision tasks are still not clear. In this work, we report the surprising discovery that, in low-illumination conditions and at high speeds, low-resolution cameras can outperform high-resolution ones, while requiring a significantly lower bandwidth. We provide both empirical and theoretical evidence for this claim, which indicates that high-resolution event cameras exhibit higher per-pixel event rates, leading to higher temporal noise in low-illumination conditions and at high speeds. As a result, in most cases, high-resolution event cameras show a lower task performance, compared to lower resolution sensors in these conditions. We empirically validate our findings across several tasks, namely image reconstruction, optical flow estimation, and camera pose tracking, both on synthetic and real data. We believe that these findings will provide important guidelines for future trends in event camera development.
    Keywords Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2022-03-28
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception

    Aydin, Asude / Gehrig, Mathias / Gehrig, Daniel / Scaramuzza, Davide

    2023  

    Abstract: Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse processing. However, being temporal models, SNNs depend heavily on ... ...

    Abstract Spiking Neural Networks (SNN) are a class of bio-inspired neural networks that promise to bring low-power and low-latency inference to edge devices through asynchronous and sparse processing. However, being temporal models, SNNs depend heavily on expressive states to generate predictions on par with classical artificial neural networks (ANNs). These states converge only after long transient periods, and quickly decay without input data, leading to higher latency, power consumption, and lower accuracy. This work addresses this issue by initializing the state with an auxiliary ANN running at a low rate. The SNN then uses the state to generate predictions with high temporal resolution until the next initialization phase. Our hybrid ANN-SNN model thus combines the best of both worlds: It does not suffer from long state transients and state decay thanks to the ANN, and can generate predictions with high temporal resolution, low latency, and low power thanks to the SNN. We show for the task of event-based 2D and 3D human pose estimation that our method consumes 88% less power with only a 4% decrease in performance compared to its fully ANN counterparts when run at the same inference rate. Moreover, when compared to SNNs, our method achieves a 74% lower error. This research thus provides a new understanding of how ANNs and SNNs can be used to maximize their respective benefits.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-03-24
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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