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
Adaptive Intermediate Representations for Video Understanding—0
VOLDOR: Visual Odometry from Log-logistic Dense Optical flow ResidualsCode1
VOLDOR-SLAM: For the Times When Feature-Based or Direct Methods Are Not Good EnoughCode1
MESD: Exploring Optical Flow Assessment on Edge of Motion Objects with Motion Edge Structure Difference—0
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline—0
ASFlow: Unsupervised Optical Flow Learning with Adaptive Pyramid Sampling—0
Progressive Temporal Feature Alignment Network for Video InpaintingCode1
Learning optical flow from still imagesCode1
Track, Check, Repeat: An EM Approach to Unsupervised Tracking—0
Deep Animation Video Interpolation in the WildCode1
Learning to Estimate Hidden Motions with Global Motion AggregationCode1
Non-contact PPG Signal and Heart Rate Estimation with Multi-hierarchical Convolutional Network—0
Learning Optical Flow from a Few MatchesCode1
PDWN: Pyramid Deformable Warping Network for Video Interpolation—0
"Forget" the Forget Gate: Estimating Anomalies in Videos using Self-contained Long Short-Term Memory Networks—0
Optical Flow Dataset Synthesis from Unpaired Images—0
Deep Two-View Structure-from-Motion RevisitedCode1
DCVNet: Dilated Cost Volume Networks for Fast Optical Flow—0
Towards Understanding Adversarial Robustness of Optical Flow NetworksCode0
XVFI: eXtreme Video Frame InterpolationCode1
Broaden Your Views for Self-Supervised Video LearningCode1
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation—0
Low-Fidelity End-to-End Video Encoder Pre-training for Temporal Action Localization—0
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark DatasetCode1
Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature AggregationCode1
COTR: Correspondence Transformer for Matching Across ImagesCode1
GyroFlow: Gyroscope-Guided Unsupervised Optical Flow LearningCode1
Efficient Regional Memory Network for Video Object SegmentationCode1
Automatic Quantification of Facial Asymmetry using Facial Landmarks—0
Fusion-FlowNet: Energy-Efficient Optical Flow Estimation using Sensor Fusion and Deep Fused Spiking-Analog Network Architectures—0
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow—0
Future Frame Prediction for Robot-assisted Surgery—0
The U-Net based GLOW for Optical-Flow-free Video Interframe Generation—0
Model-free Vehicle Tracking and State Estimation in Point Cloud SequencesCode1
FastFlowNet: A Lightweight Network for Fast Optical Flow EstimationCode1
Unsupervised Motion Representation Enhanced Network for Action Recognition—0
Multi-Stage Raw Video Denoising with Adversarial Loss and Gradient MaskCode1
Optical Flow Estimation from a Single Motion-blurred Image—0
Motion-blurred Video Interpolation and Extrapolation—0
Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features—0
DF-VO: What Should Be Learnt for Visual Odometry?Code1
Learning for Unconstrained Space-Time Video Super-Resolution—0
Estimating Nonplanar Flow from 2D Motion-blurred Widefield Microscopy Images via Deep LearningCode0
Normalized Convolution Upsampling for Refined Optical Flow EstimationCode1
Hybrid Neural Fusion for Full-frame Video StabilizationCode1
Frame Difference-Based Temporal Loss for Video StylizationCode1
Analysis of Latent-Space Motion for Collaborative Intelligence—0
Video Action Recognition Using spatio-temporal optical flow video frames—0
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection ConsistencyCode1
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products—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
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