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 251–300 of 2184 papers

TitleStatusHype
EM-driven unsupervised learning for efficient motion segmentationCode1
Motion-Focused Contrastive Learning of Video RepresentationsCode1
Motion-prior Contrast Maximization for Dense Continuous-Time Motion EstimationCode1
Motion Representation Using Residual Frames with 3D CNNCode1
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised TrainingCode1
Efficient Video Deblurring Guided by Motion MagnitudeCode1
Abstract Flow for Temporal Semantic Segmentation on the Permutohedral LatticeCode1
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance SystemsCode1
End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular CameraCode1
Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging LabelsCode1
E2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action RecognitionCode1
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical VideoCode1
Dynamic Frame Interpolation in Wavelet DomainCode1
Comparing Correspondences: Video Prediction with Correspondence-wise LossesCode1
E^2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action RecognitionCode1
Efficient Meshflow and Optical Flow Estimation from Event CamerasCode1
End-to-end Multi-modal Video Temporal GroundingCode1
Exploiting temporal and depth information for multi-frame face anti-spoofingCode1
Distracting Downpour: Adversarial Weather Attacks for Motion EstimationCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
Channel Attention Is All You Need for Video Frame InterpolationCode1
Diffeomorphic Particle Image VelocimetryCode1
DIP: Deep Inverse Patchmatch for High-Resolution Optical FlowCode1
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion ReasoningCode1
Depth-Cooperated Trimodal Network for Video Salient Object DetectionCode1
Cascaded Deep Video Deblurring Using Temporal Sharpness PriorCode1
Delving into Crispness: Guided Label Refinement for Crisp Edge DetectionCode1
CCVS: Context-aware Controllable Video SynthesisCode1
DEFLOW: Self-supervised 3D Motion Estimation of Debris FlowCode1
Dense Continuous-Time Optical Flow from Events and FramesCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Deficiency-Aware Masked Transformer for Video InpaintingCode1
E-RAFT: Dense Optical Flow from Event CamerasCode1
DF-VO: What Should Be Learnt for Visual Odometry?Code1
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event CameraCode1
3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World ModelCode1
DTVNet: Dynamic Time-lapse Video Generation via Single Still ImageCode1
DVC: An End-to-end Deep Video Compression FrameworkCode1
Event-Free Moving Object Segmentation from Moving Ego VehicleCode1
ClothFormer:Taming Video Virtual Try-on in All ModuleCode1
Deep Online Fused Video StabilizationCode1
Deep Multi-view Depth Estimation with Predicted UncertaintyCode1
CloudCast -- Total Cloud Cover Nowcasting with Machine LearningCode1
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
Efficient Regional Memory Network for Video Object SegmentationCode1
Efficient Semantic Video Segmentation with Per-frame InferenceCode1
A Perturbation-Constrained Adversarial Attack for Evaluating the Robustness of Optical FlowCode1
Deep Rotation Correction without Angle PriorCode1
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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
4LiteFlowNet-ftAverage End-Point Error5.38—Unverified
5MR-FlowAverage 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
2IRR-PWCAverage End-Point Error1.6—Unverified
3LiteFlowNet-ftAverage End-Point Error1.6—Unverified
4PWC-Net + ft - axXivAverage End-Point Error1.5—Unverified
5FDFlowNet-ftAverage End-Point Error1.5—Unverified
6SelFlowAverage End-Point Error1.5—Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4—Unverified
8LiteFlowNet3Average End-Point Error1.3—Unverified
9LiteFlowNet3-SAverage End-Point Error1.3—Unverified
10MaskFlownet-SAverage 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