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 1101–1150 of 2184 papers

TitleStatusHype
Demonstration of Vector Flow Imaging using Convolutional Neural Networks—0
Denoising-based Turbo Message Passing for Compressed Video Background Subtraction—0
Dense, Accurate Optical Flow Estimation With Piecewise Parametric Model—0
Dense Matchers for Dense Tracking—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints—0
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements—0
Dense Optical Flow Prediction from a Static Image—0
Dense Scene Flow from Stereo Disparity and Optical Flow—0
DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation—0
Depth Extraction from Video Using Non-parametric Sampling—0
DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation—0
Depth Map Estimation of Dynamic Scenes Using Prior Depth Information—0
DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling—0
Simulating the gravity in S11 parameters and friis transmission equation—0
Design of Sensor Fusion Driver Assistance System for Active Pedestrian Safety—0
Detailed Surface Geometry and Albedo Recovery from RGB-D Video Under Natural Illumination—0
Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition—0
Detail Preserving Residual Feature Pyramid Modules for Optical Flow—0
Detecting Moving Objects Using a Novel Optical-Flow-Based Range-Independent Invariant—0
Detection of (Hidden) Emotions from Videos using Muscles Movements and Face Manifold Embedding—0
Devon: Deformable Volume Network for Learning Optical Flow—0
DeVOS: Flow-Guided Deformable Transformer for Video Object Segmentation—0
DFVS: Deep Flow Guided Scene Agnostic Image Based Visual Servoing—0
Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation—0
Differentiable Forward and Backward Fixed-Point Iteration Layers—0
Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks—0
Differential Scene Flow from Light Field Gradients—0
DiffPoseNet: Direct Differentiable Camera Pose Estimation—0
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow—0
Digging Deeper into Egocentric Gaze Prediction—0
Dimensions of Motion: Monocular Prediction through Flow Subspaces—0
Direct, Dense, and Deformable: Template-Based Non-Rigid 3D Reconstruction From RGB Video—0
Direct Photometric Alignment by Mesh Deformation—0
Discovering Hidden Physics Behind Transport Dynamics—0
Discrete MRF Inference of Marginal Densities for Non-uniformly Discretized Variable Space—0
Distance Surface for Event-Based Optical Flow—0
Distinguish Any Fake Videos: Unleashing the Power of Large-scale Data and Motion Features—0
Distinguishing Posed and Spontaneous Smiles by Facial Dynamics—0
DistractFlow: Improving Optical Flow Estimation via Realistic Distractions and Pseudo-Labeling—0
Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots—0
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition—0
DNN-Driven Compressive Offloading for Edge-Assisted Semantic Video Segmentation—0
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
DressRecon: Freeform 4D Human Reconstruction from Monocular Video—0
Drift Robust Non-rigid Optical Flow Enhancement for Long Sequences—0
DroneAttention: Sparse Weighted Temporal Attention for Drone-Camera Based Activity Recognition—0
DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes—0
Dual approach for object tracking based on optical flow and swarm intelligence—0
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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