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 751–800 of 2184 papers

TitleStatusHype
Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow—0
Generalizing to the Open World: Deep Visual Odometry with Online Adaptation—0
Fast Convex Relaxations using Graph Discretizations—0
Fast and Accurate Optical Flow based Depth Map Estimation from Light Fields—0
Flexible Techniques for Differentiable Rendering with 3D Gaussians—0
FLODCAST: Flow and Depth Forecasting via Multimodal Recurrent Architectures—0
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence—0
Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC—0
Generating, Fast and Slow: Scalable Parallel Video Generation with Video Interface Networks—0
Flow-Assisted Motion Learning Network for Weakly-Supervised Group Activity Recognition—0
Flow Based Self-supervised Pixel Embedding for Image Segmentation—0
Fall Detector Adapted to Nursing Home Needs through an Optical-Flow based CNN—0
Flow-Based Visual Stream Compression for Event Cameras—0
FlowCam: Training Generalizable 3D Radiance Fields without Camera Poses via Pixel-Aligned Scene Flow—0
FlowCaps: Optical Flow Estimation with Capsule Networks For Action Recognition—0
FlowControl: Optical Flow Based Visual Servoing—0
FlowDA: Unsupervised Domain Adaptive Framework for Optical Flow Estimation—0
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation—0
ATCA: an Arc Trajectory Based Model with Curvature Attention for Video Frame Interpolation—0
Flow Distillation Sampling: Regularizing 3D Gaussians with Pre-trained Matching Priors—0
GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement—0
Flow Dynamics Correction for Action Recognition—0
Face Flow—0
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation—0
Face Animation with Multiple Source Images—0
FlowFormer: A Transformer Architecture and Its Masked Cost Volume Autoencoding for Optical Flow—0
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks—0
Coupled Recurrent Network (CRN)—0
Generalized Video Deblurring for Dynamic Scenes—0
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models—0
A Flow-Guided Mutual Attention Network for Video-Based Person Re-Identification—0
GFlow: Recovering 4D World from Monocular Video—0
Extending Visual Dynamics for Video-to-Music Generation—0
Flow Guided Recurrent Neural Encoder for Video Salient Object Detection—0
Flow-guided Semi-supervised Video Object Segmentation—0
Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation—0
Asymmetric Bilateral Phase Correlation for Optical Flow Estimation in the Frequency Domain—0
Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch—0
FlowLoss: Dynamic Flow-Conditioned Loss Strategy for Video Diffusion Models—0
Exploring Weak Stabilization for Motion Feature Extraction—0
Counting people from above: Airborne video based crowd analysis—0
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow—0
A Survey on Backbones for Deep Video Action Recognition—0
Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations—0
Exploring Self-Attention for Visual Odometry—0
Aggregation of local parametric candidates with exemplar-based occlusion handling for optical flow—0
GateHUB: Gated History Unit with Background Suppression for Online Action Detection—0
Deep Curiosity Loops in Social Environments—0
Flow supervision for Deformable NeRF—0
Exploring Optical Flow Inclusion into nnU-Net Framework for Surgical Instrument Segmentation—0
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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