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 1051–1100 of 2184 papers

TitleStatusHype
Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes—0
Learning Cross-modal Contrastive Features for Video Domain Adaptation—0
Deep Crisp Boundaries: From Boundaries to Higher-level Tasks—0
Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation—0
Deep Crisp Boundaries—0
Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization—0
DeepAVO: Efficient Pose Refining with Feature Distilling for Deep Visual Odometry—0
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach—0
Unsupervised Domain Adaptation by Optical Flow Augmentation in Semantic Segmentation—0
Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Discrimination—0
Unsupervised Fish Trajectory Tracking and Segmentation—0
Learning for Unconstrained Space-Time Video Super-Resolution—0
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition—0
Learning Fused Pixel and Feature-Based View Reconstructions for Light Fields—0
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow—0
Learning Higher-Order Dynamics in Video-Based Cardiac Measurement—0
Deep 360^ Optical Flow Estimation Based on Multi-Projection Fusion—0
ActAR: Actor-Driven Pose Embeddings for Video Action Recognition—0
Unsupervised Flow Refinement near Motion Boundaries—0
Learning Image Relations with Contrast Association Networks—0
Learning Independent Object Motion from Unlabelled Stereoscopic Videos—0
Learning Interpretable End-to-End Vision-Based Motion Planning for Autonomous Driving with Optical Flow Distillation—0
Learning Model-Blind Temporal Denoisers without Ground Truths—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning—0
Learning Motion Patterns in Videos—0
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM—0
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks—0
Learning Multi-Object Tracking and Segmentation from Automatic Annotations—0
Learning Nanoscale Motion Patterns of Vesicles in Living Cells—0
DDCNet-Multires: Effective Receptive Field Guided Multiresolution CNN for Dense Prediction—0
Learning Omnidirectional Flow in 360-degree Video via Siamese Representation—0
Learning On-Road Visual Control for Self-Driving Vehicles with Auxiliary Tasks—0
Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow—0
A Convolutional Feature Map based Deep Network targeted towards Traffic Detection and Classification—0
DDCNet: Deep Dilated Convolutional Neural Network for Dense Prediction—0
Learning Optical Flow, Depth, and Scene Flow without Real-World Labels—0
DCVNet: Dilated Cost Volume Networks for Fast Optical Flow—0
Learning Optical Flow from Event Camera with Rendered Dataset—0
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
Learning Optical Flow via Dilated Networks and Occlusion Reasoning—0
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild—0
Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
Learning Rank Reduced Interpolation with Principal Component Analysis—0
DASC: Dense Adaptive Self-Correlation Descriptor for Multi-Modal and Multi-Spectral Correspondence—0
Learning Representative Temporal Features for Action Recognition—0
Learning Residual Flow as Dynamic Motion from Stereo Videos—0
A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation—0
Learning segmentation from point trajectories—0
Show:102550
← PrevPage 22 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