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 2051–2100 of 2184 papers

TitleStatusHype
Self-supervised Learning of Motion CaptureCode0
DeepMatching: Hierarchical Deformable Dense MatchingCode0
AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision FarmingCode0
Event Cameras, Contrast Maximization and Reward Functions: An AnalysisCode0
Learning Human Optical FlowCode0
Event-based Optical Flow on Neuromorphic Processor: ANN vs. SNN Comparison based on Activation SparsificationCode0
Video Semantic Segmentation with Inter-Frame Feature Fusion and Inner-Frame Feature RefinementCode0
Kinematics Modeling Network for Video-based Human Pose EstimationCode0
Learning Energy Based Inpainting for Optical FlowCode0
Deep-learning Optical Flow Outperforms PIV in Obtaining Velocity Fields from Active NematicsCode0
TCAM: Temporal Class Activation Maps for Object Localization in Weakly-Labeled Unconstrained VideosCode0
Deep Learning for Precipitation Nowcasting: A Benchmark and A New ModelCode0
Deep Flow-Guided Video InpaintingCode0
UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census LossCode0
Action Recognition with Dynamic Image NetworksCode0
Learning End-To-End Scene Flow by Distilling Single Tasks KnowledgeCode0
Learning Dynamic Point Cloud Compression via Hierarchical Inter-frame Block MatchingCode0
Boosting Object Representation Learning via Motion and Object ContinuityCode0
Self-supervised Transfer Learning for Instance Segmentation through Physical InteractionCode0
Action Recognition for Depth Video using Multi-view Dynamic ImagesCode0
Estimating Nonplanar Flow from 2D Motion-blurred Widefield Microscopy Images via Deep LearningCode0
Deep Motion Blind Video StabilizationCode0
A Fusion Approach for Multi-Frame Optical Flow EstimationCode0
Learning Correspondence from the Cycle-Consistency of TimeCode0
A Conditional Adversarial Network for Scene Flow EstimationCode0
Deep End-to-End Alignment and Refinement for Time-of-Flight RGB-D ModuleCode0
Learning Blind Video Temporal ConsistencyCode0
Enhancing Action Recognition by Leveraging the Hierarchical Structure of Actions and Textual ContextCode0
ZS-VCOS: Zero-Shot Outperforms Supervised Video Camouflaged Object SegmentationCode0
Semantic Video CNNs through Representation WarpingCode0
Phase-Based Frame Interpolation for VideoCode0
Beyond Short Snippets: Deep Networks for Video ClassificationCode0
End-to-end Video-level Representation Learning for Action RecognitionCode0
Learnable Cost Volume Using the Cayley RepresentationCode0
PIPsUS: Self-Supervised Dense Point Tracking in UltrasoundCode0
Benchmarking the Robustness of Optical Flow Estimation to CorruptionsCode0
PIV-FlowDiffuser:Transfer-learning-based denoising diffusion models for PIVCode0
Decomposition of Optical Flow on the SphereCode0
End-to-End Learning of Representations for Asynchronous Event-Based DataCode0
LAPNet: Non-rigid Registration derived in k-space for Magnetic Resonance ImagingCode0
SENSE: a Shared Encoder Network for Scene-flow EstimationCode0
Lagrangian Motion Magnification with Double Sparse Optical Flow DecompositionCode0
End-to-End Learning of Motion Representation for Video UnderstandingCode0
Ego-motion Estimation Based on Fusion of Images and EventsCode0
Leveraging Category Information for Single-Frame Visual Sound Source SeparationCode0
Efficient Coarse-To-Fine PatchMatch for Large Displacement Optical FlowCode0
Effective Video Mirror Detection with Inconsistent Motion CuesCode0
Pose And Joint-Aware Action RecognitionCode0
An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data InterpolationCode0
EDEN: Multimodal Synthetic Dataset of Enclosed GarDEN ScenesCode0
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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
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