https://github.com/neuromorphicsystems/event_warping
공식 코드Density Invariant Contrast Maximization for Neuromorphic Earth Observations
Papers with Code 아카이브
Event-based vision
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An event camera, also known as a neuromorphic camera, silicon retina or dynamic vision sensor, is an imaging sensor that responds to local changes in brightness. Event cameras do not capture images using a shutter as conventional cameras do. Instead, each pixel inside an event camera operates independently and asynchronously, reporting changes in brightness as they occur and staying silent otherwise. Modern event cameras have microsecond temporal resolution, 120 dB dynamic range, and less under/overexposure and motion blur than frame cameras.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/njuvision/eventtransformer
공식 코드https://github.com/pbonazzi/eva
공식 코드Towards Low-Latency Event-based Obstacle Avoidance on a FPGA-Drone
https://github.com/peterande/get-group-event-transformer
공식 코드https://github.com/queahren/demosaicformer
공식 코드DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Camera
https://github.com/realtime-intelligence/evrt-detr
공식 코드EvRT-DETR: Latent Space Adaptation of Image Detectors for Event-based Vision
https://github.com/ruizhao26/ste-flownet
공식 코드Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation
https://github.com/shicy17/ido-vfi
공식 코드IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with Events
https://github.com/shrutarv/mtevent_toolkit
공식 코드MTevent: A Multi-Task Event Camera Dataset for 6D Pose Estimation and Moving Object Detection
https://github.com/tub-rip/event_based_bos
공식 코드https://github.com/tub-rip/event_based_optical_flow
공식 코드https://github.com/tub-rip/event_collapse
공식 코드A Fast Geometric Regularizer to Mitigate Event Collapse in the Contrast Maximization Framework
https://github.com/tub-rip/visual_stabilization
공식 코드On the Benefits of Visual Stabilization for Frame- and Event-based Perception
https://github.com/tudelft/cuSNN
공식 코드https://github.com/tudelft/idnet
공식 코드Lightweight Event-based Optical Flow Estimation via Iterative Deblurring
https://github.com/tum-vision/mem
공식 코드Masked Event Modeling: Self-Supervised Pretraining for Event Cameras
https://github.com/uzh-rpg/event-based_vision_resources
공식 코드https://github.com/uzh-rpg/event_based_ping_pong_ball_trajectory_prediction
공식 코드Egocentric Event-Based Vision for Ping Pong Ball Trajectory Prediction
https://github.com/uzh-rpg/event_representation_study
공식 코드From Chaos Comes Order: Ordering Event Representations for Object Recognition and Detection
https://github.com/uzh-rpg/rpg_ev-transfer
공식 코드Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
https://github.com/uzh-rpg/rvt
공식 코드Recurrent Vision Transformers for Object Detection with Event Cameras
https://github.com/uzh-rpg/ssms_event_cameras
공식 코드https://github.com/vbrebion/rt_of_low_high_res_event_cameras
공식 코드Real-Time Optical Flow for Vehicular Perception with Low- and High-Resolution Event Cameras
https://github.com/vincentqqu/evrepsl
공식 코드EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision
https://github.com/vlislab22/Deep-Learning-for-Event-based-Vision
공식 코드Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks
https://github.com/wponghiran/temporally_dense_flow
공식 코드Event-based Temporally Dense Optical Flow Estimation with Sequential Learning
https://github.com/yingfuxu/ann_vs_snn_evflow
공식 코드https://github.com/yitian97/SDformerFlow
공식 코드SDformerFlow: Spatiotemporal swin spikeformer for event-based optical flow estimation
https://github.com/ziweiWWANG/AKF
공식 코드https://gitlab.com/europeanspaceagency/trajectory-to-events
공식 코드On the Generation of a Synthetic Event-Based Vision Dataset for Navigation and Landing
https://github.com/ChillTerry/GS-EVT
GS-EVT: Cross-Modal Event Camera Tracking based on Gaussian Splatting
https://github.com/LinZhu111/EVSNN
Event-based Video Reconstruction via Potential-assisted Spiking Neural Network
https://github.com/TimoStoff/event_cnn_minimal
https://github.com/deepspike/tandem_learning
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
https://github.com/dfki-av/anonynoise
Person Re-Identification without Identification via Event Anonymization
https://github.com/lindermanlab/S5
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.