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computer-vision-6

Event-Based Video Reconstruction

3260 papers • 126 benchmarks • 313 datasets

Event-Based Video Reconstruction aims to generate a sequence of intensity frames from an asynchronous stream of events (per-pixel brightness change signals outputted by an event camera).

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Event-Camera Dataset

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Most implemented papers

Reducing the Sim-to-Real Gap for Event Cameras

D. Scaramuzza, C. Scheerlinck, Timo Stoffregen, Nick Barnes, R. Mahony, T. Drummond, L. Kleeman•Thu Mar 19 2020

This work presents a new High Quality Frames (HQF) dataset, containing events and ground truth frames from a DAVIS240C that are well-exposed and minimally motion-blurred, and presents strategies for improving training data for event based CNNs that result in 20-40% boost in performance of existing state-of-the-art (SOTA) video reconstruction networks retrained with this method.

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Introduction Benchmarks Datasets Subtasks Libraries Papers

216

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An Asynchronous Kalman Filter for Hybrid Event Cameras

C. Scheerlinck, R. Mahony, Ziwei Wang, Yonhon Ng•Wed Dec 09 2020

The proposed algorithm includes a frame augmentation pre-processing step that deblurs and temporally interpolates frame data using events and outperforms state-of-the-art methods in both absolute intensity error and image similarity indexes.

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EVREAL: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction

Aykut Erdem, Erkut Erdem, Burak Ercan, O. Eker•Sat Apr 29 2023

This paper proposes a unified evaluation methodology and introduces an open-source framework called EVREAL to comprehensively benchmark and analyze various event-based video reconstruction methods from the literature and provides valuable insights into the performance of these methods under varying settings, challenging scenarios, and downstream tasks.

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Event-based Video Reconstruction Using Transformer

Zhiwei Xiong, Wenming Weng, Yueyi Zhang•Thu Sep 30 2021

Event cameras, which output events by detecting spatio- temporal brightness changes, bring a novel paradigm to image sensors with high dynamic range and low latency. Previous works have achieved impressive performances on event-based video reconstruction by introducing convolutional neural networks (CNNs). However, intrinsic locality of convolutional operations is not capable of modeling long-range dependency, which is crucial to many vision tasks. In this paper, we present a hybrid CNN- Transformer network for event-based video reconstruction (ET-Net), which merits the fine local information from CNN and global contexts from Transformer In addition, we further propose a Token Pyramid Aggregation strategy to implement multi-scale token integration for relating internal and intersected semantic concepts in the token-space. Experimental results demonstrate that our proposed method achieves superior performance over state-of-the-art methods on multiple real-world event datasets. The code is available at https://github.com/WarranWeng/ET-Net.

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Event-based Video Reconstruction via Potential-assisted Spiking Neural Network

Tiejun Huang, Yonghong Tian, Jianing Li, Lin Zhu, Xiao Wang, Yi Chang•Mon Jan 24 2022

This paper proposes a novel Event-based Video reconstruction framework based on a fully Spiking Neural Network (EVSNN), which utilizes Leaky-Integrate-and-Fire (LIF) neuron and Membrane Potential (MP) neuron, and finds that the spiking neurons have the potential to store useful temporal information (memory) to complete such time-dependent tasks.

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HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks

Aykut Erdem, Erkut Erdem, Burak Ercan, O. Eker, Canberk Saglam•Tue May 09 2023

This study proposes HyperE2VID, a dynamic neural network architecture for event-based video reconstruction that uses hypernetworks to generate per-pixel adaptive filters guided by a context fusion module that combines information from event voxel grids and previously reconstructed intensity images.

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Event-Based Video Reconstruction | State-of-the-Art