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
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
NOLO: Navigate Only Look Once—0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
RainMamba: Enhanced Locality Learning with State Space Models for Video DerainingCode2
SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting—0
Event-based Optical Flow on Neuromorphic Processor: ANN vs. SNN Comparison based on Activation SparsificationCode0
Revisit Event Generation Model: Self-Supervised Learning of Event-to-Video Reconstruction with Implicit Neural Representations—0
Occlusion-Aware 3D Motion Interpretation for Abnormal Behavior Detection—0
SAFNet: Selective Alignment Fusion Network for Efficient HDR ImagingCode1
Real-Time Image Analysis Software Suitable for Resource-Constrained ComputingCode0
Many Perception Tasks are Highly Redundant Functions of their Input Data—0
Attenuation-Aware Weighted Optical Flow with Medium Transmission Map for Learning-based Visual Odometry in Underwater terrainCode0
Fusion Flow-enhanced Graph Pooling Residual Networks for Unmanned Aerial Vehicles Surveillance in Day and Night Dual Visions—0
ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality AssessmentCode1
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves—0
Temporal Event Stereo via Joint Learning with Stereoscopic FlowCode1
Motion-prior Contrast Maximization for Dense Continuous-Time Motion EstimationCode1
Research Experience of an Undergraduate Student in Computer Vision and Robotics—0
ScaleFlow++: Robust and Accurate Estimation of 3D Motion from VideoCode1
Event-based vision on FPGAs -- a survey—0
Let Occ Flow: Self-Supervised 3D Occupancy Flow Prediction—0
Unsupervised 4D Cardiac Motion Tracking with Spatiotemporal Optical Flow Networks—0
CardioSpectrum: Comprehensive Myocardium Motion Analysis with 3D Deep Learning and Geometric InsightsCode0
Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene ReconstructionCode2
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration ModelsCode2
What Matters in Detecting AI-Generated Videos like Sora?—0
A Universal Railway Obstacle Detection System based on Semi-supervised Segmentation And Optical Flow—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
Disentangled Motion Modeling for Video Frame InterpolationCode2
Splatter a Video: Video Gaussian Representation for Versatile Processing—0
Low Latency Visual Inertial Odometry with On-Sensor Accelerated Optical Flow for Resource-Constrained UAVs—0
MEDeA: Multi-view Efficient Depth Adjustment—0
Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion—0
PLT-D3: A High-fidelity Dynamic Driving Simulation Dataset for Stereo Depth and Scene Flow—0
Blur-aware Spatio-temporal Sparse Transformer for Video DeblurringCode2
Ada-VE: Training-Free Consistent Video Editing Using Adaptive Motion PriorCode0
A Semi-Self-Supervised Approach for Dense-Pattern Video Object Segmentation—0
Reanimating Images using Neural Representations of Dynamic Stimuli—0
DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation—0
Self-Calibrating 4D Novel View Synthesis from Monocular Videos Using Gaussian SplattingCode0
Enhancing Dynamic CT Image Reconstruction with Neural Fields and Optical Flow—0
Synthetic Data Generation for 3D Myocardium Deformation AnalysisCode0
Prototypical Transformer as Unified Motion Learners—0
EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos—0
May the Dance be with You: Dance Generation Framework for Non-Humanoids—0
GFlow: Recovering 4D World from Monocular Video—0
Flow-Assisted Motion Learning Network for Weakly-Supervised Group Activity Recognition—0
SCSim: A Realistic Spike Cameras SimulatorCode0
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
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All datasetsSintel-cleanSintel-finalKITTI 2015 (train)KITTI 2015KITTI 2012SpringSintel Clean unsupervisedSintel Final unsupervisedKITTI 2015 unsupervisedKITTI 2012 unsupervised

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