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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 2184 papers

TitleStatusHype
Distracting Downpour: Adversarial Weather Attacks for Motion EstimationCode1
A Simple Detector with Frame Dynamics is a Strong TrackerCode1
DIP: Deep Inverse Patchmatch for High-Resolution Optical FlowCode1
DVC: An End-to-end Deep Video Compression FrameworkCode1
Diffeomorphic Particle Image VelocimetryCode1
Depth-Cooperated Trimodal Network for Video Salient Object DetectionCode1
AccFlow: Backward Accumulation for Long-Range Optical FlowCode1
E-RAFT: Dense Optical Flow from Event CamerasCode1
Dense Continuous-Time Optical Flow from Events and FramesCode1
DEFLOW: Self-supervised 3D Motion Estimation of Debris FlowCode1
Deficiency-Aware Masked Transformer for Video InpaintingCode1
Delving into Crispness: Guided Label Refinement for Crisp Edge DetectionCode1
Dynamic Frame Interpolation in Wavelet DomainCode1
Deep Rotation Correction without Angle PriorCode1
Deep Online Fused Video StabilizationCode1
DF-VO: What Should Be Learnt for Visual Odometry?Code1
Deep Slow Motion Video Reconstruction with Hybrid Imaging SystemCode1
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-ReflectorsCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration NetworkCode1
Deep Multi-view Depth Estimation with Predicted UncertaintyCode1
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
Event-Free Moving Object Segmentation from Moving Ego VehicleCode1
Deep Two-View Structure-from-Motion RevisitedCode1
3D Multi-frame Fusion for Video StabilizationCode1
Deep Burst Super-ResolutionCode1
Texture-guided Saliency Distilling for Unsupervised Salient Object DetectionCode1
Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging LabelsCode1
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical VideoCode1
A Transductive Approach for Video Object SegmentationCode1
Efficient Regional Memory Network for Video Object SegmentationCode1
EM-driven unsupervised learning for efficient motion segmentationCode1
Deep Equilibrium Optical Flow EstimationCode1
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
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression TasksCode1
End-to-End Video Object Detection with Spatial-Temporal TransformersCode1
Decoupling Dynamic Monocular Videos for Dynamic View SynthesisCode1
Entropy Minimisation Framework for Event-based Vision Model EstimationCode1
Beyond a Video Frame Interpolator: A Space Decoupled Learning Approach to Continuous Image TransitionCode1
A Unified Pyramid Recurrent Network for Video Frame InterpolationCode1
AirSim Drone Racing LabCode1
EVLoc: Event-based Visual Localization in LiDAR Maps via Event-Depth RegistrationCode1
D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion PredictionCode1
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-ResolutionCode1
AutoFlow: Learning a Better Training Set for Optical FlowCode1
A Large Scale Event-based Detection Dataset for AutomotiveCode1
FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based PolicyCode1
Deep Animation Video Interpolation in the WildCode1
Show:102550
← PrevPage 4 of 44Next →
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