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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1151–1200 of 2184 papers

TitleStatusHype
Duality-Gated Mutual Condition Network for RGBT Tracking—0
Dual Semantic Fusion Network for Video Object Detection—0
DualX-VSR: Dual Axial SpatialTemporal Transformer for Real-World Video Super-Resolution without Motion Compensation—0
A Semi-Self-Supervised Approach for Dense-Pattern Video Object Segmentation—0
Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments—0
Dyadic Movement Synchrony Estimation Under Privacy-preserving Conditions—0
Dynamic 3D Avatar Creation from Hand-held Video Input—0
Dynamical optical flow of saliency maps for predicting visual attention—0
Dynamic Dense RGB-D SLAM using Learning-based Visual Odometry—0
Dynamic Event-based Optical Identification and Communication—0
Dynamic Facial Expression Recognition under Partial Occlusion with Optical Flow Reconstruction—0
Dynamic Scene Deblurring using a Locally Adaptive Linear Blur Model—0
Dynamic semantic VSLAM with known and unknown objects—0
Early Action Recognition with Action Prototypes—0
EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation—0
EDeNN: Event Decay Neural Networks for low latency vision—0
Edge SLAM: Edge Points Based Monocular Visual SLAM—0
Edit as You See: Image-guided Video Editing via Masked Motion Modeling—0
Efficient Correlation Volume Sampling for Ultra-High-Resolution Optical Flow Estimation—0
Efficient Dynamic Scene Deblurring Using Spatially Variant Deconvolution Network With Optical Flow Guided Training—0
3D-CNN for Facial Micro- and Macro-expression Spotting on Long Video Sequences using Temporal Oriented Reference Frame—0
Efficient Masked AutoEncoder for Video Object Counting and A Large-Scale Benchmark—0
Efficient Neuromorphic Signal Processing with Loihi 2—0
Efficient Sparse-to-Dense Optical Flow Estimation Using a Learned Basis and Layers—0
Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors—0
Efficient Two-Stream Motion and Appearance 3D CNNs for Video Classification—0
Efficient Unsupervised Video Object Segmentation Network Based on Motion Guidance—0
Efficient Video Segmentation Models with Per-frame Inference—0
Efficient Video Semantic Segmentation with Labels Propagation and Refinement—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
Effortless Cross-Platform Video Codec: A Codebook-Based Method—0
Egocentric Height Estimation—0
Egocentric Vision Language Planning—0
Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera—0
Ego-motion Sensor for Unmanned Aerial Vehicles Based on a Single-Board Computer—0
EMAG: Ego-motion Aware and Generalizable 2D Hand Forecasting from Egocentric Videos—0
Emotion-Controllable Generalized Talking Face Generation—0
Emotion Recognition from the perspective of Activity Recognition—0
EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation—0
EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting—0
Endoscopic Depth Measurement and Super-Spectral-Resolution Imaging—0
Endo-TTAP: Robust Endoscopic Tissue Tracking via Multi-Facet Guided Attention and Hybrid Flow-point Supervision—0
End to end collision avoidance based on optical flow and neural networks—0
End-to-end Flow Correlation Tracking with Spatial-temporal Attention—0
End-to-End Interactive Prediction and Planning with Optical Flow Distillation for Autonomous Driving—0
End-to-End Learning for Video Frame Compression with Self-Attention—0
End-to-End Learning of Video Super-Resolution with Motion Compensation—0
End-to-end Neural Video Coding Using a Compound Spatiotemporal Representation—0
End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention—0
ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs—0
Show:102550
← PrevPage 24 of 44Next →
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
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