RESEARCH PAPERS

We introduce EventLFM, a straightforward and cost-effective system that overcomes these challenges by integrating an event camera with Fourier light field microscopy (LFM), a state-of-the-art single-shot 3D wide-field imaging technique. We further develop a simple and robust event-driven LFM reconstruction algorithm that can reliably reconstruct 3D dynamics from the unique spatiotemporal measurements captured by EventLFM.

The experimental evaluation on a public dataset demonstrates that the proposed fixed-length coding framework provides at least two times the compression ratio relative to the raw EF representation and a close performance compared with variable-length video coding standards and variable-length state-of-the-art image codecs for lossless compression of ternary EFs generated at frequencies below one KHz.

This paper presents a novel technique for perceiving air convection using events and frames by providing the first theoretical analysis that connects event data and schlieren. We formulate the problem as a variational optimization one combining the linearized event generation model with a physically-motivated parameterization that estimates the temporal derivative of the air density.

Resident space objects in the size range of 0.1 mm–3 cm are not currently trackable but have enough kinetic energy to have lethal consequences for spacecraft. The assessment of small orbital debris, potentially posing a risk to most space missions, requires the combination of a large sensor area and large time coverage.

We benchmark our model against other event-graph and convolutional neural network based approaches on the challenging DVS-Lip dataset (spoken word classification). We find that not only does our method outperform state of the art approaches for similar model sizes, but that, relative to the convolutional models, the number of calculation operations per second was reduced by 81%.

To explore the potential of event cameras, Ultraleap have developed a prototype stereo camera using two Prophesee IMX636ES sensors. To go from event data to hand positions the event data is aggregated into event frames. This is then consumed by a hand tracking model which outputs 28 joint positions for each hand with respect to the camera.

We present a new approach to direct depth estimation for Spatial Augmented Reality (SAR) applications using event cameras. These dynamic vision sensors are a great fit to be paired with laser projectors for depth estimation in a structured light approach. Our key contributions involve a conversion of the projector time map into a rectified X-map, capturing x-axis correspondences for incoming events and enabling direct disparity lookup without any additional search.

We present the first events-only static-obstacle avoidance method for a quadrotor with just an onboard, monocular event camera. By leveraging depth prediction as an intermediate step in our learning framework, we can pre-train a reactive obstacle avoidance events-to-control policy in simulation, and then fine-tune the perception component with limited events-depth real-world data to achieve dodging in indoor and outdoor settings.

In this work, we propose a dual-camera system consisting of an event camera and a conventional RGB camera for video motion magnification, providing temporally-dense information from the event stream and spatially-dense data from the RGB images. This innovative combination enables a broad and cost-effective amplification of high-frequency motions.

Our previous work demonstrated the early development of neuromorphic imaging cytometry, evaluating its feasibility in resolving conventional frame-based imaging systems’ limitations in data redundancy, fluorescence sensitivity, and compromised throughput. Herein, we adopted a convolutional spiking neural network (SNN) combined with the YOLOv3 model (SNN-YOLO) to perform cell classification and detection on label-free samples under neuromorphic vision.

We introduce a method to detect full RGB events using a monochrome EC aided by a structured light projector. We combine the benefits of ECs and projection-based techniques and allow depth and color detection of static or moving objects with a commercial TI LightCrafter 4500 projector and a monocular monochrome EC, paving the way for frameless RGB-D sensing applications.

We propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation. Our EIF-BiOFNet utilizes each valuable characteristic of the events and images for direct estimation of inter-frame motion fields without any approximation methods. We develop an interactive attention-based frame synthesis network to efficiently leverage the complementary warping-based and synthesis-based features.

Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition.

In this work, we propose Robust e-NeRF, a novel method to directly and robustly reconstruct NeRFs from moving event cameras under various real-world conditions, especially from sparse and noisy events generated under non-uniform motion.

In this paper, we propose a novel hierarchical knowledge distillation framework that can fully utilize multi-modal / multi-view information during training to facilitate knowledge transfer, enabling us to achieve high-speed and low-latency visual tracking during testing by using only event signals.

This study proposes an event filter-based phase correlation template match (EF-PCTM) method, including its optimal design procedure, to measure micro-vibrations using an event camera. In this study, event filter is designed using an infinite impulse response filter and genetic algorithm, and a cost function is defined to improve the performance of EF-PCTM.

Faces in Event Streams dataset contains 689 minutes of recorded event streams, and 1.6 million annotated faces with bounding box and five point facial landmarks. This paper presents the dataset and corresponding models for detecting face and facial landmakrs directly from event stream data.

In this paper, we present an asynchronous linear filter architecture, fusing event and frame camera data, for HDR video reconstruction and spatial convolution that exploits the advantages of both sensor modalities. The key idea is the introduction of a state that directly encodes the integrated or convolved image information and that is updated asynchronously as each event or each frame arrives from the camera.

We show that our proposed pipeline provides improved accuracy over the result of the state-of-the-art visual odometry for stereo event-based cameras, while running in real-time on a standard CPU (low-resolution cameras). To the best of our knowledge, this is the first published visual-inertial odometry for stereo event-based cameras.

In this paper, we propose a novel approach by integrating a bio-inspired event camera into the unsupervised video deraining pipeline, which enables us to capture high temporal resolution information and model complex rain characteristics. Specifically, we first design an end-to-end learning-based network consisting of two modules, the asymmetric separation module and the cross-modal fusion module.

Our work has achieved highly consistent outputs with a widely adopted flow cytometer (CytoFLEX) in detecting microparticles. Moreover, the capacity of an event-based photosensor in registering fluorescent signals was evaluated by recording 6 µm Fluorescein isothiocyanate-marked particles in different lighting conditions, revealing superior performance compared to a standard photosensor.

In this paper, we propose the soft epipolar 3D(SEpi-3D) method to eliminate multipath in temporal space with an event camera and a laser projector. Specifically, we align the projector and event camera row onto the same epipolar plane with stereo rectification; we capture event flow synchronized with the projector frame to construct a mapping relationship between event timestamp and projector pixel.

This paper explores the unique membrane potential dynamics of SNNs and their ability to modulate sparse events. We introduce an innovative spike-triggered adaptive threshold mechanism designed for stable training. Building on these insights, we present a specialized spiking feature pyramid network (SpikeFPN) optimized for automotive event-based object detection.

In this paper, we present a novel event-based structured light system that enables fast 3D imaging of mixed reflectance scenes with high accuracy. On the captured events, we use epipolar constraints that intrinsically enable decomposing the measured reflections into diffuse, two-bounce specular, and other multi-bounce reflections.

This paper presents an open-source aerial neuromorphic dataset that captures pedestrians and vehicles moving in an urban environment. The dataset, titled NU-AIR, features 70.75 minutes of event footage acquired with a 640 x 480 resolution neuromorphic sensor mounted on a quadrotor operating in an urban environment.

This work quantitatively evaluates a multi-modal camera setup for fusing high-resolution DVS data with RGB image data by static camera alignment. The proposed setup, which is intended for semi-automatic DVS data labeling, combines two recently released Prophesee EVK4 DVS cameras and one global shutter XIMEA MQ022CG-CM RGB camera.

We present M3ED, the first multi-sensor event camera dataset focused on high-speed dynamic motions in robotics applications. M3ED provides high-quality synchronized and labeled data from multiple platforms, including ground vehicles, legged robots, and aerial robots, operating in challenging conditions such as driving along off-road trails, navigating through dense forests, and executing aggressive flight maneuvers.

This paper presents F-UAV-D (Fast Unmanned Aerial Vehicle Detector), an embedded system that enables fast-moving drone detection. In particular, we propose a setup to exploit DVS as an alternative to RGB cameras in a real-time and low-power configuration. Our approach leverages the high-dynamic range (HDR) and background suppression of DVS.

To investigate the causes of declining insect populations, a monitoring system is needed that automatically records insect activity and additional environmental factors over an extended period of time. For this reason, we use a sensor-based method with two event cameras. In this paper, we describe the system, the view volume that can be recorded with it, and a database used for insect detection.

Deep Event Visual Odometry sparsely tracks selected event patches over time. A key component of DEVO is a novel deep patch selection mechanism tailored to event data. We significantly decrease the pose tracking error on seven real-world benchmarks by up to 97% compared to event-only methods and often surpass or are close to stereo or inertial methods.

This paper focuses on five event-aided image and video enhancement tasks (i.e., event-based video reconstruction, event-aided high frame rate video reconstruction, image deblurring, image super-resolution, and high dynamic range image reconstruction), provides an analysis of the effects of different event properties, a real-captured and ground truth labeled benchmark dataset.

The proposed system is designed using a transmitter-centric information-theoretic criterion that targets a reduction of the communication overhead, while retaining the most relevant information for the end-to-end semantic task of interest. Numerical results on standard data sets validate the proposed architecture, and a preliminary testbed realization is reported.

Our model outperforms by a large margin feed-forward event-based architectures. Moreover, our method does not require any reconstruction of intensity images from events, showing that training directly from raw events is possible, more efficient, and more accurate than passing through an intermediate intensity image.

Our model outperforms by a large margin feed-forward event-based architectures. Moreover, our method does not require any reconstruction of intensity images from events, showing that training directly from raw events is possible, more efficient, and more accurate than passing through an intermediate intensity image.

Our neuromorphic fingertip tactile sensor, NeuTouch, scales well with the number of taxels thanks to its event-based nature. Likewise, our Visual-Tactile Spiking Neural Network (VT-SNN) enables fast perception when coupled with event sensors. We evaluate our visual-tactile system (using the NeuTouch and Prophesee event camera) on two robot tasks.

We propose a generic event camera calibration framework using image reconstruction. Instead of relying on blinking patterns or external screens, we show that neural network-based image reconstruction is well suited for the task of intrinsic and extrinsic calibration of event cameras.

Event cameras operate at low power ( 5mW) and respond to changes in the scene with a latency on the order of microseconds. These properties make event cameras an exciting candidate for eye tracking sensors on mobile platforms such as AR/VR headsets, since these systems have hard real-time and power constraints.

In this study, Prophesee introduces the first very large detection dataset for event cameras. The dataset is composed of more than 39 hours of automotive recordings acquired with a 304×240 GEN1 sensor. It contains open roads and very diverse driving scenarios, ranging from urban, highway, suburbs and countryside scenes.

Our model outperforms by a large margin feed-forward event-based architectures. Moreover, our method does not require any reconstruction of intensity images from events, showing that training directly from raw events is possible, more efficient, and more accurate than passing through an intermediate intensity image.

In this work, we present experimental results of a high-speed label-free imaging cytometry system that seamlessly merges the high-capturing rate and data sparsity of an event-based CMOS camera with lightweight photonic neuromorphic processing. The results confirm that neuromorphic sensing and neuromorphic computing can be efficiently merged to a unified bio-inspired system, offering a holistic enhancement in emerging bio-imaging applications.