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 101–150 of 2184 papers

TitleStatusHype
Efficient Semantic Video Segmentation with Per-frame InferenceCode1
Aligning First, Then Fusing: A Novel Weakly Supervised Multimodal Violence Detection MethodCode1
A Lightweight Recurrent Aggregation Network for Satellite Video Super-ResolutionCode1
Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging LabelsCode1
Counting People by Estimating People FlowsCode1
CRAFT: Cross-Attentional Flow Transformer for Robust Optical FlowCode1
Creating Artificial Modalities to Solve RGB LivenessCode1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
Volterra Neural Networks (VNNs)Code1
Efficient Video Deblurring Guided by Motion MagnitudeCode1
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical VideoCode1
Unified Domain Adaptive Semantic SegmentationCode1
Adaptive Multi-source Predictor for Zero-shot Video Object SegmentationCode1
A Large Scale Event-based Detection Dataset for AutomotiveCode1
D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion PredictionCode1
Deep Animation Video Interpolation in the WildCode1
Decoupling Dynamic Monocular Videos for Dynamic View SynthesisCode1
An Attention-guided Multistream Feature Fusion Network for Localization of Risky Objects in Driving VideosCode1
360° Optical Flow using Tangent ImagesCode1
Deep Learning for Event-based Vision: A Comprehensive Survey and BenchmarksCode1
Event-based Background-Oriented SchlierenCode1
Deep Equilibrium Optical Flow EstimationCode1
An Empirical Study of End-to-End Video-Language Transformers with Masked Visual ModelingCode1
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow EstimationCode1
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration NetworkCode1
E-VFIA : Event-Based Video Frame Interpolation with AttentionCode1
Conditional Object-Centric Learning from VideoCode1
AirSim Drone Racing LabCode1
Efficient Meshflow and Optical Flow Estimation from Event CamerasCode1
EndoMUST: Monocular Depth Estimation for Robotic Endoscopy via End-to-end Multi-step Self-supervised TrainingCode1
Exploiting temporal and depth information for multi-frame face anti-spoofingCode1
DTVNet: Dynamic Time-lapse Video Generation via Single Still ImageCode1
DVC: An End-to-end Deep Video Compression FrameworkCode1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame PredictionCode1
CloudCast -- Total Cloud Cover Nowcasting with Machine LearningCode1
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
Dynamic Frame Interpolation in Wavelet DomainCode1
Chasing Day and Night: Towards Robust and Efficient All-Day Object Detection Guided by an Event CameraCode1
ClothFormer:Taming Video Virtual Try-on in All ModuleCode1
Event-Free Moving Object Segmentation from Moving Ego VehicleCode1
Channel Attention Is All You Need for Video Frame InterpolationCode1
3D Multi-frame Fusion for Video StabilizationCode1
Comparing Correspondences: Video Prediction with Correspondence-wise LossesCode1
DS-Net: Dynamic Spatiotemporal Network for Video Salient Object DetectionCode1
E^2(GO)MOTION: Motion Augmented Event Stream for Egocentric Action RecognitionCode1
Cascaded Deep Video Deblurring Using Temporal Sharpness PriorCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion ReasoningCode1
Distracting Downpour: Adversarial Weather Attacks for Motion EstimationCode1
BurstM: Deep Burst Multi-scale SR using Fourier Space with Optical FlowCode1
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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