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 1651–1700 of 2184 papers

TitleStatusHype
Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion—0
Robust Optical Flow Computation: A Higher-Order Differential Approach—0
Robust Optical Flow Estimation in Rainy Scenes—0
Robust Optical Flow Estimation of Double-Layer Images under Transparency or Reflection—0
Robust Optical Flow in Rainy Scenes—0
A Simple Baseline for Pose Tracking in Videos of Crowded Scenes—0
RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo—0
Robust Two-Stream Multi-Feature Network for Driver Drowsiness Detection—0
Robust Video Stabilization by Optimization in CNN Weight Space—0
Learning Generalized Visual Odometry Using Position-Aware Optical Flow and Geometric Bundle Adjustment—0
ASFlow: Unsupervised Optical Flow Learning with Adaptive Pyramid Sampling—0
Rolling-Shutter-Aware Differential SfM and Image Rectification—0
A Semi-Automated Method for Object Segmentation in Infant's Egocentric Videos to Study Object Perception—0
Toward Better Understanding of Saliency Prediction in Augmented 360 Degree Videos—0
Video Frame Interpolation via Structure-Motion based Iterative Fusion—0
RoMo: Robust Motion Segmentation Improves Structure from Motion—0
RoPECraft: Training-Free Motion Transfer with Trajectory-Guided RoPE Optimization on Diffusion Transformers—0
A Robust Lane Detection and Departure Warning System—0
RST-MODNet: Real-time Spatio-temporal Moving Object Detection for Autonomous Driving—0
S2DM: Sector-Shaped Diffusion Models for Video Generation—0
S2F: Slow-To-Fast Interpolator Flow—0
S^3Track: Self-supervised Tracking with Soft Assignment Flow—0
A robust and efficient video representation for action recognition—0
3DPeople: Modeling the Geometry of Dressed Humans—0
Saliency-guided video classification via adaptively weighted learning—0
Saliency-Motion Guided Trunk-Collateral Network for Unsupervised Video Object Segmentation—0
A Reinforcement-Learning-Based Energy-Efficient Framework for Multi-Task Video Analytics Pipeline—0
Salient Sparse Visual Odometry With Pose-Only Supervision—0
Video Frame Interpolation with Stereo Event and Intensity Camera—0
SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model—0
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution—0
SAVE: Protagonist Diversification with Structure Agnostic Video Editing—0
SC3EF: A Joint Self-Correlation and Cross-Correspondence Estimation Framework for Visible and Thermal Image Registration—0
Scalable Full Flow with Learned Binary Descriptors—0
Scale-aware Two-stage High Dynamic Range Imaging—0
Are High-Resolution Event Cameras Really Needed?—0
A real-time algorithm for human action recognition in RGB and thermal video—0
Scale-Space Flow for End-to-End Optimized Video Compression—0
Scaling Properties of Diffusion Models for Perceptual Tasks—0
Scene Motion Decomposition for Learnable Visual Odometry—0
YOIO: You Only Iterate Once by mining and fusing multiple necessary global information in the optical flow estimation—0
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-Training on Indoor Segmentation?—0
Architecture Search of Dynamic Cells for Semantic Video Segmentation—0
SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations—0
A qualitative investigation of optical flow algorithms for video denoising—0
YoTube: Searching Action Proposal via Recurrent and Static Regression Networks—0
Video Interpolation and Prediction with Unsupervised Landmarks—0
Video Interpolation by Event-driven Anisotropic Adjustment of Optical Flow—0
Approaches, Challenges, and Applications for Deep Visual Odometry: Toward to Complicated and Emerging Areas—0
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
Show:102550
← PrevPage 34 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
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