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 1001–1050 of 2184 papers

TitleStatusHype
Two-dimensional flow field measurement of sediment-laden flow based on ultrasound image velocimetry—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Learning To Segment Dominant Object Motion From Watching Videos—0
GMFlow: Learning Optical Flow via Global MatchingCode2
Conditional Object-Centric Learning from VideoCode1
Monocular Arbitrary Moving Object Discovery and SegmentationCode1
Hierarchical interaction network for video object segmentation from referring expressions—0
Learning Generalized Visual Odometry Using Position-Aware Optical Flow and Geometric Bundle Adjustment—0
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation—0
FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical FlowCode1
CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow EstimationCode1
MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather—0
Enhanced Correlation Matching based Video Frame Interpolation—0
Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking—0
Consistent Semantic Attacks on Optical Flow—0
Self-Supervised Real-time Video Stabilization—0
FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based PolicyCode1
Triple-level Model Inferred Collaborative Network Architecture for Video DerainingCode0
ROFT: Real-Time Optical Flow-Aided 6D Object Pose and Velocity TrackingCode1
Efficient Neuromorphic Signal Processing with Loihi 2—0
KORSAL: Key-point Detection based Online Real-Time Spatio-Temporal Action LocalizationCode0
Video Salient Object Detection via Contrastive Features and Attention Modules—0
Joint Detection of Motion Boundaries and OcclusionsCode0
AuxAdapt: Stable and Efficient Test-Time Adaptation for Temporally Consistent Video Semantic SegmentationCode1
IVS3D: An Open Source Framework for Intelligent Video Sampling and Preprocessing to Facilitate 3D ReconstructionCode1
HDRVideo-GAN: Deep Generative HDR Video Reconstruction—0
UVO Challenge on Video-based Open-World Segmentation 2021: 1st Place SolutionCode1
Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation—0
Multi-Object Tracking and Segmentation with a Space-Time Memory Network—0
ReFIn: A Refinement Approach for Video Frame Interpolation—0
Temporally stable video segmentation without video annotations—0
"Knights": First Place Submission for VIPriors21 Action Recognition Challenge at ICCV 2021—0
Talking Detection In Collaborative Learning Environments—0
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-ReflectorsCode1
Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution—0
Quadrotor going through a window and landing: An image-based visual servo control approach—0
Optical Flow Estimation for Spiking CameraCode1
Learning Higher-Order Dynamics in Video-Based Cardiac Measurement—0
Automatic Identification of the End-Diastolic and End-Systolic Cardiac Frames from Invasive Coronary Angiography Videos—0
Accelerated First Order Methods for Variational ImagingCode0
Sensor-Guided Optical FlowCode1
Video Forgery Detection Using Multiple Cues on Fusion of EfficientNet and Swin Transformer—0
Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table TennisCode1
PDC-Net+: Enhanced Probabilistic Dense Correspondence NetworkCode1
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical VideoCode1
A Novel Factor Graph-Based Optimization Technique for Stereo Correspondence Estimation—0
TempNet -- Temporal Super Resolution of Radar Rainfall Products with Residual CNNs—0
LOF: Structure-Aware Line Tracking based on Optical Flow—0
RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo MatchingCode2
What happens in Face during a facial expression? Using data mining techniques to analyze facial expression motion vectors—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
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