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 951–1000 of 2184 papers

TitleStatusHype
Unsupervised Learning Optical Flow in Multi-frame Dynamic Environment Using Temporal Dynamic Modeling—0
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor Environments—0
MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer—0
Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch—0
Co-attention Propagation Network for Zero-Shot Video Object SegmentationCode0
A real-time algorithm for human action recognition in RGB and thermal video—0
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots—0
Multi-view reconstruction of bullet time effect based on improved NSFF model—0
CapsFlow: Optical Flow Estimation with Capsule Networks—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation—0
Flow supervision for Deformable NeRF—0
SUDS: Scalable Urban Dynamic Scenes—0
HQ3DAvatar: High Quality Controllable 3D Head Avatar—0
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow—0
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling—0
Learning Optical Flow from Event Camera with Rendered Dataset—0
Video shutter angle estimation using optical flow and linear blur—0
Mutual Information-Based Temporal Difference Learning for Human Pose Estimation in Video—0
BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation—0
PATS: Patch Area Transportation with Subdivision for Local Feature Matching—0
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow—0
Dynamic Event-based Optical Identification and Communication—0
Scale-aware Two-stage High Dynamic Range Imaging—0
DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball ImageCode0
Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural networkCode0
3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective—0
Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow—0
Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network—0
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
Texture-Based Input Feature Selection for Action Recognition—0
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
RipViz: Finding Rip Currents by Learning Pathline Behavior—0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms—0
Multi-scale Motion-Aware Module for Video Action Recognition—0
A Cloud-based Deep Learning Framework for Early Detection of Pushing at Crowded Event EntrancesCode0
One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2—0
AI pipeline for accurate retinal layer segmentation using OCT 3D images—0
Spatiotemporal Deformation Perception for Fisheye Video RectificationCode0
NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM—0
DFlow: Learning to Synthesize Better Optical Flow Datasets via a Differentiable PipelineCode0
Uncertainty-Driven Dense Two-View Structure from Motion—0
Optical Flow Estimation in 360^ Videos: Dataset, Model and Application—0
Flow-guided Semi-supervised Video Object Segmentation—0
GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning—0
Spatio-Temporal Context Modeling for Road Obstacle Detection—0
Deep Dynamic Scene Deblurring from Optical Flow—0
Optical Flow for Autonomous Driving: Applications, Challenges and Improvements—0
Video Semantic Segmentation with Inter-Frame Feature Fusion and Inner-Frame Feature RefinementCode0
Show:102550
← PrevPage 20 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