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 351–400 of 2184 papers

TitleStatusHype
DVC: An End-to-end Deep Video Compression FrameworkCode1
Deep Equilibrium Optical Flow EstimationCode1
CRAFT: Cross-Attentional Flow Transformer for Robust Optical FlowCode1
Creating Artificial Modalities to Solve RGB LivenessCode1
Learning to Track Objects from Unlabeled VideosCode1
Dynamic Frame Interpolation in Wavelet DomainCode1
Efficient Semantic Video Segmentation with Per-frame InferenceCode1
Less is More: Consistent Video Depth Estimation with Masked Frames ModelingCode1
Deep Online Fused Video StabilizationCode1
Lightweight Event-based Optical Flow Estimation via Iterative DeblurringCode1
Event-based Temporally Dense Optical Flow Estimation with Sequential LearningCode1
LiteFlowNet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow EstimationCode1
A Transductive Approach for Video Object SegmentationCode1
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference ModelingCode1
Masked Motion Encoding for Self-Supervised Video Representation LearningCode1
CSFlow: Learning Optical Flow via Cross Strip Correlation for Autonomous DrivingCode1
Deep Rotation Correction without Angle PriorCode1
DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost VolumeCode1
AnimeRun: 2D Animation Visual Correspondence from Open Source 3D MoviesCode1
MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion PerceptionCode1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame PredictionCode1
Deep Slow Motion Video Reconstruction with Hybrid Imaging SystemCode1
Attention-guided Network for Ghost-free High Dynamic Range ImagingCode1
MFTIQ: Multi-Flow Tracker with Independent Matching Quality EstimationCode1
Unified Domain Adaptive Semantic SegmentationCode1
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion DetectionCode1
MMASD: A Multimodal Dataset for Autism Intervention AnalysisCode1
CamLiFlow: Bidirectional Camera-LiDAR Fusion for Joint Optical Flow and Scene Flow EstimationCode1
Event-Free Moving Object Segmentation from Moving Ego VehicleCode1
Model-free Vehicle Tracking and State Estimation in Point Cloud SequencesCode1
MOL: Joint Estimation of Micro-Expression, Optical Flow, and Landmark via Transformer-Graph-Style ConvolutionCode1
Monocular Arbitrary Moving Object Discovery and SegmentationCode1
Displacement-Invariant Matching Cost Learning for Accurate Optical Flow EstimationCode1
Animation from Blur: Multi-modal Blur Decomposition with Motion GuidanceCode1
D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion PredictionCode1
A Unified Pyramid Recurrent Network for Video Frame InterpolationCode1
AirSim Drone Racing LabCode1
Deep Video Matting via Spatio-Temporal Alignment and AggregationCode1
Decoupling Dynamic Monocular Videos for Dynamic View SynthesisCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical FlowCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Deep Animation Video Interpolation in the WildCode1
Multi-Modal Domain Adaptation for Fine-Grained Action RecognitionCode1
Deep Burst Super-ResolutionCode1
AutoFlow: Learning a Better Training Set for Optical FlowCode1
A Large Scale Event-based Detection Dataset for AutomotiveCode1
DIP: Deep Inverse Patchmatch for High-Resolution Optical FlowCode1
Distracting Downpour: Adversarial Weather Attacks for Motion EstimationCode1
DS-Net: Dynamic Spatiotemporal Network for Video Salient Object DetectionCode1
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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