Search Results for author:
Found 25 papers, 15 papers with code
An N-Point Linear Solver for Line and Motion Estimation with Event Cameras
To recover the full linear camera velocity we fuse observations from multiple lines with a novel velocity averaging scheme that relies on a geometrically-motivated residual, and thus solves the problem more efficiently than previous schemes which minimize an algebraic residual.
A 5-Point Minimal Solver for Event Camera Relative Motion Estimation
Event-based cameras are ideal for line-based motion estimation, since they predominantly respond to edges in the scene.
End-to-end Learned Visual Odometry with Events and Frames
Visual Odometry (VO) is crucial for autonomous robotic navigation, especially in GPS-denied environments like planetary terrains.
E-Calib: A Fast, Robust and Accurate Calibration Toolbox for Event Cameras
However, conventional image-based calibration techniques are not applicable due to the asynchronous, binary output of the sensor.
From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection
However, selecting the appropriate representation for the task traditionally requires training a neural network for each representation and selecting the best one based on the validation score, which is very time-consuming.
Ranked #1 on
Object Detection
on GEN1 Detection
A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perception
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.
Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras
A recent line of work tackles this issue by modeling events as spatiotemporally evolving graphs that can be efficiently and asynchronously processed using graph neural networks.
Exploring Event Camera-based Odometry for Planetary Robots
Due to their resilience to motion blur and high robustness in low-light and high dynamic range conditions, event cameras are poised to become enabling sensors for vision-based exploration on future Mars helicopter missions.
Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion
Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory efficiency.
AEGNN: Asynchronous Event-based Graph Neural Networks
For this reason, recent works have adopted Graph Neural Networks (GNNs), which process events as ``static" spatio-temporal graphs, which are inherently "sparse".
Are High-Resolution Event Cameras Really Needed?
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.
ESS: Learning Event-based Semantic Segmentation from Still Images
Nonetheless, semantic segmentation with event cameras is still in its infancy which is chiefly due to the lack of high-quality, labeled datasets.
Ranked #5 on
Event-based Object Segmentation
on MVSEC-SEG
Multi-Bracket High Dynamic Range Imaging with Event Cameras
Modern high dynamic range (HDR) imaging pipelines align and fuse multiple low dynamic range (LDR) images captured at different exposure times.
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
However, event-based vision has been held back by the shortage of labeled datasets due to the novelty of event cameras.
E-RAFT: Dense Optical Flow from Event Cameras
Modern frame-based optical flow methods heavily rely on matching costs computed from feature correlation.
Time Lens: Event-Based Video Frame Interpolation
However, while these approaches can capture non-linear motions they suffer from ghosting and perform poorly in low-texture regions with few events.
TimeLens: Event-based Video Frame Interpolation
State-of-the-art frame interpolation methods generate intermediate frames by inferring object motions in the image from consecutive key-frames.
How to Calibrate Your Event Camera
We propose a generic event camera calibration framework using image reconstruction.
DSEC: A Stereo Event Camera Dataset for Driving Scenarios
To address these challenges, we propose, DSEC, a new dataset that contains such demanding illumination conditions and provides a rich set of sensory data.
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction
However, events only measure the varying component of the visual signal, which limits their ability to encode scene context.
Learning Monocular Dense Depth from Events
Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous events instead of intensity frames.
Event-based Asynchronous Sparse Convolutional Networks
However, these approaches discard the spatial and temporal sparsity inherent in event data at the cost of higher computational complexity and latency.
Video to Events: Recycling Video Datasets for Event Cameras
Event cameras are novel sensors that output brightness changes in the form of a stream of asynchronous "events" instead of intensity frames.
End-to-End Learning of Representations for Asynchronous Event-Based Data
Event cameras are vision sensors that record asynchronous streams of per-pixel brightness changes, referred to as "events".
Ranked #15 on
Robust classification
on N-ImageNet
Asynchronous, Photometric Feature Tracking using Events and Frames
By contrast, standard cameras provide intensity measurements (frames) that do not depend on motion direction.
