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 1151–1200 of 2184 papers

TitleStatusHype
CoSEC: A Coaxial Stereo Event Camera Dataset for Autonomous Driving—0
Convective regularization for optical flow—0
A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution—0
LLA-FLOW: A Lightweight Local Aggregation on Cost Volume for Optical Flow Estimation—0
Local Attention Transformers for High-Detail Optical Flow Upsampling—0
Controllable Animation of Fluid Elements in Still Images—0
Contour Flow: Middle-Level Motion Estimation by Combining Motion Segmentation and Contour Alignment—0
Local Layering for Joint Motion Estimation and Occlusion Detection—0
Unsupervised Learning of Multi-Frame Optical Flow with Occlusions—0
LOF: Structure-Aware Line Tracking based on Optical Flow—0
Log-Euclidean Bag of Words for Human Action Recognition—0
Unsupervised learning of object frames by dense equivariant image labelling—0
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
LoSA: Long-Short-range Adapter for Scaling End-to-End Temporal Action Localization—0
Low-Fidelity End-to-End Video Encoder Pre-training for Temporal Action Localization—0
Low-Fidelity Video Encoder Optimization for Temporal Action Localization—0
Low-Latency Human Action Recognition with Weighted Multi-Region Convolutional Neural Network—0
Low Latency Visual Inertial Odometry with On-Sensor Accelerated Optical Flow for Resource-Constrained UAVs—0
Unsupervised Learning of Optical Flow with Deep Feature Similarity—0
LSM: Learning Subspace Minimization for Low-level Vision—0
Accurate Optical Flow via Direct Cost Volume Processing—0
Continual Occlusions and Optical Flow Estimation—0
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation—0
Continual Learning of Conjugated Visual Representations through Higher-order Motion Flows—0
Machine Learning for Precipitation Nowcasting from Radar Images—0
Unsupervised Learning Optical Flow in Multi-frame Dynamic Environment Using Temporal Dynamic Modeling—0
Making a Case for Learning Motion Representations with Phase—0
Context-aware Synthesis for Video Frame Interpolation—0
MambaFlow: A Mamba-Centric Architecture for End-to-End Optical Flow Estimation—0
Many Perception Tasks are Highly Redundant Functions of their Input Data—0
Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation—0
Markerless Tracking-Based Registration for Medical Image Motion Correction—0
Unsupervised Motion Representation Enhanced Network for Action Recognition—0
Context-Aware Input Orchestration for Video Inpainting—0
A Framework for Fluid Motion Estimation using a Constraint-Based Refinement Approach—0
Consistent Semantic Attacks on Optical Flow—0
Mask Propagation Network for Video Object Segmentation—0
F^2Depth: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis—0
Connect the Dots: In Situ 4D Seismic Monitoring of CO2 Storage with Spatio-temporal CNNs—0
May the Dance be with You: Dance Generation Framework for Non-Humanoids—0
MC-JEPA: A Joint-Embedding Predictive Architecture for Self-Supervised Learning of Motion and Content Features—0
Confused Modulo Projection based Somewhat Homomorphic Encryption -- Cryptosystem, Library and Applications on Secure Smart Cities—0
Mean Oriented Riesz Features for Micro Expression Classification—0
MEDeA: Multi-view Efficient Depth Adjustment—0
MED-VT++: Unifying Multimodal Learning with a Multiscale Encoder-Decoder Video Transformer—0
Conformalized Multimodal Uncertainty Regression and Reasoning—0
Unsupervised motion saliency map estimation based on optical flow inpainting—0
Disentangling Architecture and Training for Optical Flow—0
MemFlow: Optical Flow Estimation and Prediction with Memory—0
Confidence-guided Adaptive Gate and Dual Differential Enhancement for Video Salient Object Detection—0
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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