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 1601–1650 of 2184 papers

TitleStatusHype
MRF Optimization with Separable Convex Prior on Partially Ordered Labels—0
Mutual Suppression Network for Video Prediction using Disentangled Features—0
MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather—0
Multi-Action Recognition via Stochastic Modelling of Optical Flow and Gradients—0
Multidimensional Digital Filters for Point-Target Detection in Cluttered Infrared Scenes—0
Multi-Frame GAN: Image Enhancement for Stereo Visual Odometry in Low Light—0
Non-contact PPG Signal and Heart Rate Estimation with Multi-hierarchical Convolutional Network—0
Multi Modal Adaptive Normalization for Audio to Video Generation—0
Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detection—0
Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography—0
Multi-modal Egocentric Activity Recognition using Audio-Visual Features—0
Multimodal feature fusion for CNN-based gait recognition: an empirical comparison—0
Multi-modal Fusion and Query Refinement Network for Video Moment Retrieval and Highlight Detection—0
Multimodal Open-Vocabulary Video Classification via Pre-Trained Vision and Language Models—0
Multi-Modal Synergistic Implicit Image Enhancement for Efficient Optical Flow Estimation—0
Multi-Object Tracking and Segmentation with a Space-Time Memory Network—0
Multiple Object Tracking with Motion and Appearance Cues—0
Multi-Scale Generalized Plane Match for Optical Flow—0
Multi-scale Motion-Aware Module for Video Action Recognition—0
Multi-Scale Video Frame-Synthesis Network with Transitive Consistency Loss—0
Multi-Stream Single Shot Spatial-Temporal Action Detection—0
FLAME-based Multi-View 3D Face Reconstruction—0
Multi-View Image Fusion—0
Multi-view reconstruction of bullet time effect based on improved NSFF model—0
Multi-way Particle Swarm Fusion—0
Music Gesture for Visual Sound Separation—0
MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation—0
Mutual Information-Based Temporal Difference Learning for Human Pose Estimation in Video—0
Mutual-Structure for Joint Filtering—0
MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior—0
MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing—0
MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones—0
NanoFlowNet: Real-time Dense Optical Flow on a Nano Quadcopter—0
NccFlow: Unsupervised Learning of Optical Flow With Non-occlusion from Geometry—0
ν-DBA: Neural Implicit Dense Bundle Adjustment Enables Image-Only Driving Scene Reconstruction—0
Nested Motion Descriptors—0
Neural Cell Video Synthesis via Optical-Flow Diffusion—0
Neural Ganglion Sensors: Learning Task-specific Event Cameras Inspired by the Neural Circuit of the Human Retina—0
Neural Image Representations for Multi-Image Fusion and Layer Separation—0
Neural Inter-Frame Compression for Video Coding—0
Neural Particle Image Velocimetry—0
Reanimating Images using Neural Representations of Dynamic Stimuli—0
Neural UpFlow: A Scene Flow Learning Approach to Increase the Apparent Resolution of Particle-Based Liquids—0
Neural Video Compression using Spatio-Temporal Priors—0
Neural World Models for Computer Vision—0
Neuroexplicit Diffusion Models for Inpainting of Optical Flow Fields—0
Neuromorphic Optical Flow and Real-time Implementation with Event Cameras—0
Neuromorphic spatiotemporal optical flow: Enabling ultrafast visual perception beyond human capabilities—0
Neuro-Optimization: Learning Objective Functions Using Neural Networks—0
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow—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