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 51–100 of 2184 papers

TitleStatusHype
SDCNet: Video Prediction Using Spatially-Displaced ConvolutionCode2
ParticleSfM: Exploiting Dense Point Trajectories for Localizing Moving Cameras in the WildCode2
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge DevicesCode2
Particle Video Revisited: Tracking Through Occlusions Using Point TrajectoriesCode2
MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow EstimationCode2
Learning Optical Flow and Scene Flow with Bidirectional Camera-LiDAR FusionCode2
Breaking of brightness consistency in optical flow with a lightweight CNN networkCode2
Learning to See Through ObstructionsCode2
MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian SplattingCode2
CroCo v2: Improved Cross-view Completion Pre-training for Stereo Matching and Optical FlowCode2
GMFlow: Learning Optical Flow via Global MatchingCode2
Dense Optical Tracking: Connecting the DotsCode2
Blur-aware Spatio-temporal Sparse Transformer for Video DeblurringCode2
Free-SurGS: SfM-Free 3D Gaussian Splatting for Surgical Scene ReconstructionCode2
FLATTEN: optical FLow-guided ATTENtion for consistent text-to-video editingCode2
FlowFormer: A Transformer Architecture for Optical FlowCode2
InstMove: Instance Motion for Object-centric Video SegmentationCode2
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-SupervisionCode2
EchoTracker: Advancing Myocardial Point Tracking in EchocardiographyCode2
EDEN: Enhanced Diffusion for High-quality Large-motion Video Frame InterpolationCode2
Exploiting Optical Flow Guidance for Transformer-Based Video InpaintingCode2
FlowDiffuser: Advancing Optical Flow Estimation with Diffusion ModelsCode2
Flow-Guided Diffusion for Video InpaintingCode2
Disentangled Motion Modeling for Video Frame InterpolationCode2
DPU: Dynamic Prototype Updating for Multimodal Out-of-Distribution DetectionCode2
DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration ModelsCode2
IFRNet: Intermediate Feature Refine Network for Efficient Frame InterpolationCode2
BiM-VFI: directional Motion Field-Guided Frame Interpolation for Video with Non-uniform MotionsCode2
CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular FusionCode2
E-RAFT: Dense Optical Flow from Event CamerasCode1
Delving into Crispness: Guided Label Refinement for Crisp Edge DetectionCode1
360° Optical Flow using Tangent ImagesCode1
Dense Continuous-Time Optical Flow from Events and FramesCode1
Depth-Cooperated Trimodal Network for Video Salient Object DetectionCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Deficiency-Aware Masked Transformer for Video InpaintingCode1
Adaptive Multi-source Predictor for Zero-shot Video Object SegmentationCode1
UVO Challenge on Video-based Open-World Segmentation 2021: 1st Place SolutionCode1
Deep Video Matting via Spatio-Temporal Alignment and AggregationCode1
DEFLOW: Self-supervised 3D Motion Estimation of Debris FlowCode1
Deep Online Fused Video StabilizationCode1
Deep Multi-view Depth Estimation with Predicted UncertaintyCode1
Deep Rotation Correction without Angle PriorCode1
Deep Linear Array Pushbroom Image Restoration: A Degradation Pipeline and Jitter-Aware Restoration NetworkCode1
Deep Learning for Event-based Vision: A Comprehensive Survey and BenchmarksCode1
DeepMoCap: Deep Optical Motion Capture Using Multiple Depth Sensors and Retro-ReflectorsCode1
Deep Slow Motion Video Reconstruction with Hybrid Imaging SystemCode1
Deep Animation Video Interpolation in the WildCode1
D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion PredictionCode1
Decoupling Dynamic Monocular Videos for Dynamic View SynthesisCode1
Show:102550
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All datasetsSintel-cleanSintel-finalKITTI 2015 (train)KITTI 2015KITTI 2012SpringSintel Clean unsupervisedSintel Final unsupervisedKITTI 2015 unsupervisedKITTI 2012 unsupervised

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