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 951–1000 of 2184 papers

TitleStatusHype
Evolving Losses for Unlabeled Video Representation Learning—0
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
Evolvement Constrained Adversarial Learning for Video Style Transfer—0
Image-based monitoring of bolt loosening through deep-learning-based integrated detection and tracking—0
Continual Occlusions and Optical Flow Estimation—0
DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation—0
A Simple Baseline for Pose Tracking in Videos of Crowded Scenes—0
Evolution of Robust High Speed Optical-Flow-Based Landing for Autonomous MAVs—0
EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-based Optical Flow with Hybrid Motion-Compensation Loss—0
Continual Learning of Conjugated Visual Representations through Higher-order Motion Flows—0
A Framework for Learning Invariant Physical Relations in Multimodal Sensory Processing—0
Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation—0
Learning Cross-modal Contrastive Features for Video Domain Adaptation—0
Learning for Unconstrained Space-Time Video Super-Resolution—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
Improving Deep Video Compression by Resolution-adaptive Flow Coding—0
Improving Discriminative Multi-Modal Learning with Large-Scale Pre-Trained Models—0
Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback—0
Improving Multiple Object Tracking with Optical Flow and Edge Preprocessing—0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
In defense of OSVOS—0
Independent Sign Language Recognition with 3D Body, Hands, and Face Reconstruction—0
Context-aware Synthesis for Video Frame Interpolation—0
BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events—0
EVIMO2: An Event Camera Dataset for Motion Segmentation, Optical Flow, Structure from Motion, and Visual Inertial Odometry in Indoor Scenes with Monocular or Stereo Algorithms—0
Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation—0
Instance Embedding Transfer to Unsupervised Video Object Segmentation—0
Instance Flow Based Online Multiple Object Tracking—0
A Flow-based Credibility Metric for Safety-critical Pedestrian Detection—0
Learned Video Compression via Heterogeneous Deformable Compensation Network—0
Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion—0
Intention-driven Ego-to-Exo Video Generation—0
Interaction Detection Between Vehicles and Vulnerable Road Users: A Deep Generative Approach with Attention—0
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding—0
Inter-BMV: Interpolation with Block Motion Vectors for Fast Semantic Segmentation on Video—0
Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network—0
Context-Aware Input Orchestration for Video Inpainting—0
A Framework for Fluid Motion Estimation using a Constraint-Based Refinement Approach—0
Intrinsic Depth: Improving Depth Transfer With Intrinsic Images—0
Invariant-based Mapping of Space During General Motion of an Observer—0
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow—0
ASFlow: Unsupervised Optical Flow Learning with Adaptive Pyramid Sampling—0
3D Fluid Flow Reconstruction Using Compact Light Field PIV—0
Investigation of moving objects through atmospheric turbulence from a non-stationary platform—0
Learned Video Compression via Joint Spatial-Temporal Correlation Exploration—0
INV-Flow2PoseNet: Light-Resistant Rigid Object Pose from Optical Flow of RGB-D Images using Images, Normals and Vertices—0
A Semi-Automated Method for Object Segmentation in Infant's Egocentric Videos to Study Object Perception—0
Consistent Semantic Attacks on Optical Flow—0
Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences—0
EventHDR: from Event to High-Speed HDR Videos and Beyond—0
Show:102550
← PrevPage 20 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