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 151–200 of 2184 papers

TitleStatusHype
COTR: Correspondence Transformer for Matching Across ImagesCode1
Event Collapse in Contrast Maximization FrameworksCode1
Creating Artificial Modalities to Solve RGB LivenessCode1
Conditional Object-Centric Learning from VideoCode1
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
Deep Multi-view Depth Estimation with Predicted UncertaintyCode1
FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based PolicyCode1
Beyond the Field-of-View: Enhancing Scene Visibility and Perception with Clip-Recurrent TransformerCode1
End-to-end Multi-modal Video Temporal GroundingCode1
Volterra Neural Networks (VNNs)Code1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
Comparing Correspondences: Video Prediction with Correspondence-wise LossesCode1
AccFlow: Backward Accumulation for Long-Range Optical FlowCode1
End-to-End Video Object Detection with Spatial-Temporal TransformersCode1
Counting People by Estimating People FlowsCode1
CRAFT: Cross-Attentional Flow Transformer for Robust Optical FlowCode1
CloudCast -- Total Cloud Cover Nowcasting with Machine LearningCode1
A Simple Detector with Frame Dynamics is a Strong TrackerCode1
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular CameraCode1
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous DrivingCode1
Unified Domain Adaptive Semantic SegmentationCode1
Explicit Motion Disentangling for Efficient Optical Flow EstimationCode1
Enhanced Bi-directional Motion Estimation for Video Frame InterpolationCode1
Asynchronous Multi-Object Tracking with an Event CameraCode1
3D Multi-frame Fusion for Video StabilizationCode1
Decoupling Dynamic Monocular Videos for Dynamic View SynthesisCode1
FacialFlowNet: Advancing Facial Optical Flow Estimation with a Diverse Dataset and a Decomposed ModelCode1
Deep Animation Video Interpolation in the WildCode1
Deep Equilibrium Optical Flow EstimationCode1
A Transductive Approach for Video Object SegmentationCode1
Deep Burst Super-ResolutionCode1
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event CameraCode1
Texture-guided Saliency Distilling for Unsupervised Salient Object DetectionCode1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame PredictionCode1
Attention-guided Network for Ghost-free High Dynamic Range ImagingCode1
ClothFormer:Taming Video Virtual Try-on in All ModuleCode1
Flow-based Video Segmentation for Human Head and ShouldersCode1
CCVS: Context-aware Controllable Video SynthesisCode1
Channel Attention Is All You Need for Video Frame InterpolationCode1
Deep Rotation Correction without Angle PriorCode1
A Unified Pyramid Recurrent Network for Video Frame InterpolationCode1
AirSim Drone Racing LabCode1
Deep Slow Motion Video Reconstruction with Hybrid Imaging SystemCode1
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow EstimationCode1
FlowNet: Learning Optical Flow with Convolutional NetworksCode1
AutoFlow: Learning a Better Training Set for Optical FlowCode1
A Large Scale Event-based Detection Dataset for AutomotiveCode1
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised TrainingCode1
Entropy Minimisation Framework for Event-based Vision Model EstimationCode1
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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