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Found 22 papers, 18 papers with code
State Space Models for Event Cameras
We address this challenge by introducing state-space models (SSMs) with learnable timescale parameters to event-based vision.
LEOD: Label-Efficient Object Detection for Event Cameras
On 1Mpx, RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels.
Revisiting Token Pruning for Object Detection and Instance Segmentation
In relation to the dense counterpart that utilizes all tokens, our method realizes an increase in inference speed, achieving up to 34% faster performance for the entire network and 46% for the backbone.
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
Neuromorphic Optical Flow and Real-time Implementation with Event Cameras
Optical flow provides information on relative motion that is an important component in many computer vision pipelines.
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.
Recurrent Vision Transformers for Object Detection with Event Cameras
By revisiting the high-level design of recurrent vision backbones, we reduce inference time by a factor of 6 while retaining similar performance.
Data-driven Feature Tracking for Event Cameras
Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios.
Dense Continuous-Time Optical Flow from Events and Frames
To the best of our knowledge, our model is the first method that can regress dense pixel trajectories from event data.
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.
AlphaPilot: Autonomous Drone Racing
This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning.
Event-Based Angular Velocity Regression with Spiking Networks
Due to their spike-based computational model, SNNs can process output from event-based, asynchronous sensors without any pre-processing at extremely lower power unlike standard artificial neural networks.
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.
Towards Low-Latency High-Bandwidth Control of Quadrotors using Event Cameras
In particular, this paper addresses the problem of one-dimensional attitude tracking using a dualcopter platform equipped with an event camera.
Robotics Systems and Control Systems and Control
Focus Is All You Need: Loss Functions For Event-based Vision
The proposed loss functions allow bringing mature computer vision tools to the realm of event cameras.
Visual Place Recognition with Probabilistic Vertex Voting
We propose a novel scoring concept for visual place recognition based on nearest neighbor descriptor voting and demonstrate how the algorithm naturally emerges from the problem formulation.
