SOTAVerified

Optical Flow Estimation

Optical Flow Estimation is a computer vision task that involves computing the motion of objects in an image or a video sequence. The goal of optical flow estimation is to determine the movement of pixels or features in the image, which can be used for various applications such as object tracking, motion analysis, and video compression.

Approaches for optical flow estimation include correlation-based, block-matching, feature tracking, energy-based, and more recently gradient-based.

Further readings:

Definition source: Devon: Deformable Volume Network for Learning Optical Flow

Image credit: Optical Flow Estimation

Papers

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Showing 101–150 of 2184 papers

TitleStatusHype
Event-Free Moving Object Segmentation from Moving Ego VehicleCode1
Aligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection MethodCode1
A Lightweight Recurrent Aggregation Network for Satellite Video Super-ResolutionCode1
DIP: Deep Inverse Patchmatch for High-Resolution Optical FlowCode1
Diffeomorphic Particle Image VelocimetryCode1
A Large Scale Event-based Detection Dataset for AutomotiveCode1
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow EstimationCode1
DTVNet: Dynamic Time-lapse Video Generation via Single Still ImageCode1
Dynamic Frame Interpolation in Wavelet DomainCode1
E^2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action RecognitionCode1
AirSim Drone Racing LabCode1
Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging LabelsCode1
Adaptive Multi-source Predictor for Zero-shot Video Object SegmentationCode1
Efficient Meshflow and Optical Flow Estimation from Event CamerasCode1
Efficient Video Deblurring Guided by Motion MagnitudeCode1
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance SystemsCode1
A Simple Detector with Frame Dynamics is a Strong TrackerCode1
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving VideosCode1
360° Optical Flow using Tangent ImagesCode1
End-to-End Video Object Detection with Spatial-Temporal TransformersCode1
Entropy Minimisation Framework for Event-based Vision Model EstimationCode1
Estimating People Flows to Better Count Them in Crowded ScenesCode1
Asynchronous Multi-Object Tracking with an Event CameraCode1
Event Collapse in Contrast Maximization FrameworksCode1
EventHPE: Event-based 3D Human Pose and Shape EstimationCode1
E-VFIA : Event-Based Video Frame Interpolation with AttentionCode1
DF-VO: What Should Be Learnt for Visual Odometry?Code1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Exploiting temporal and depth information for multi-frame face anti-spoofingCode1
Dense Continuous-Time Optical Flow from Events and FramesCode1
E-RAFT: Dense Optical Flow from Event CamerasCode1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame PredictionCode1
DEFLOW: Self-supervised 3D Motion Estimation of Debris FlowCode1
Delving into Crispness: Guided Label Refinement for Crisp Edge DetectionCode1
Deep Video Matting via Spatio-Temporal Alignment and AggregationCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Deep Slow Motion Video Reconstruction with Hybrid Imaging SystemCode1
3D Multi-frame Fusion for Video StabilizationCode1
Deep Two-View Structure-from-Motion RevisitedCode1
Deficiency-Aware Masked Transformer for Video InpaintingCode1
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-ReflectorsCode1
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration NetworkCode1
Deep Multi-view Depth Estimation with Predicted UncertaintyCode1
Deep Equilibrium Optical Flow EstimationCode1
Deep Burst Super-ResolutionCode1
Deep Learning for Event-based Vision: A Comprehensive Survey and BenchmarksCode1
Deep Online Fused Video StabilizationCode1
D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion PredictionCode1
AccFlow: Backward Accumulation for Long-Range Optical FlowCode1
Decoupling Dynamic Monocular Videos for Dynamic View SynthesisCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error6.64—Unverified
2FastFlowNet-ftAverage End-Point Error4.89—Unverified
3UnrolledCostAverage End-Point Error4.69—Unverified
4LiteFlowNet-ftAverage End-Point Error4.54—Unverified
5FlowNet2Average End-Point Error3.96—Unverified
6IRR-PWCAverage End-Point Error3.84—Unverified
7SelFlowAverage End-Point Error3.74—Unverified
8FDFlowNet-ftAverage End-Point Error3.71—Unverified
9ScopeFlowAverage End-Point Error3.59—Unverified
10LiteFlowNet2-ftAverage End-Point Error3.48—Unverified
#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error8.36—Unverified
2FastFlowNet-ftAverage End-Point Error6.08—Unverified
3UnrolledCostAverage End-Point Error5.8—Unverified
4MR-FlowAverage End-Point Error5.38—Unverified
5LiteFlowNet-ftAverage End-Point Error5.38—Unverified
6FDFlowNet-ftAverage End-Point Error5.11—Unverified
7LiteFlowNet2-ftAverage End-Point Error4.69—Unverified
8IRR-PWCAverage End-Point Error4.58—Unverified
9LiteFlowNet3-SAverage End-Point Error4.53—Unverified
10ContinualFlow + ftAverage End-Point Error4.52—Unverified
#ModelMetricClaimedVerifiedStatus
1PWC-NetF1-all33.7—Unverified
2FastFlowNetF1-all33.1—Unverified
3FlowNet2F1-all30—Unverified
4VCNF1-all25.1—Unverified
5HD3F1-all24—Unverified
6MaskFlowNetF1-all23.1—Unverified
7SCVF1-all19.3—Unverified
8RAPIDFlowF1-all17.7—Unverified
9CRAFTF1-all17.5—Unverified
10RAFTF1-all17.4—Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftFl-all11.22—Unverified
2UnrolledCostFl-all10.81—Unverified
3LiteFlowNet-ftFl-all9.38—Unverified
4SelFlowFl-all8.42—Unverified
5IRR-PWCFl-all7.65—Unverified
6LiteFlowNet2-ftFl-all7.62—Unverified
7LiteFlowNet3Fl-all7.34—Unverified
8LiteFlowNet3-SFl-all7.22—Unverified
9MaskFlownet-SFl-all6.81—Unverified
10RAPIDFlowFl-all6.12—Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftAverage End-Point Error1.8—Unverified
2LiteFlowNet-ftAverage End-Point Error1.6—Unverified
3IRR-PWCAverage End-Point Error1.6—Unverified
4SelFlowAverage End-Point Error1.5—Unverified
5FDFlowNet-ftAverage End-Point Error1.5—Unverified
6PWC-Net + ft - axXivAverage End-Point Error1.5—Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4—Unverified
8LiteFlowNet3-SAverage End-Point Error1.3—Unverified
9LiteFlowNet3Average End-Point Error1.3—Unverified
10MaskFlownetAverage End-Point Error1.1—Unverified
#ModelMetricClaimedVerifiedStatus
1PWCNet1px total82.27—Unverified
2SPyNet1px total29.96—Unverified
3GMFlow1px total10.36—Unverified
4GMA1px total7.07—Unverified
5RAFT1px total6.79—Unverified
6FlowNet21px total6.71—Unverified
7FlowFormer1px total6.51—Unverified
8MS-RAFT+1px total5.72—Unverified
9RPKNet1px total4.81—Unverified
10DPFlow1px total3.44—Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error5.21—Unverified
2MDFlow-FastAverage End-Point Error4.73—Unverified
3UpFlowAverage End-Point Error4.68—Unverified
4ARFlow-MVAverage End-Point Error4.49—Unverified
5MDFlowAverage End-Point Error4.16—Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error6.5—Unverified
2MDFlow-FastAverage End-Point Error5.99—Unverified
3ARFlow-MVAverage End-Point Error5.67—Unverified
4MDFlowAverage End-Point Error5.46—Unverified
5UpFlowAverage End-Point Error5.32—Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVFl-all11.79—Unverified
2MDFlow-FastFl-all11.43—Unverified
3UpFlowFl-all9.38—Unverified
4MDFlowFl-all8.91—Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVAverage End-Point Error1.5—Unverified
2UpFlowAverage End-Point Error1.4—Unverified