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 801–850 of 2184 papers

TitleStatusHype
An End-to-End Two-Stream Network Based on RGB Flow and Representation Flow for Human Action Recognition—0
Differential Scene Flow from Light Field Gradients—0
Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks—0
Bregman Iteration for Correspondence Problems: A Study of Optical Flow—0
Differentiable Forward and Backward Fixed-Point Iteration Layers—0
Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation—0
Breaking The Ice: Video Segmentation for Close-Range Ice-Covered Waters—0
An End-to-End Edge Aggregation Network for Moving Object Segmentation—0
DFVS: Deep Flow Guided Scene Agnostic Image Based Visual Servoing—0
Boundary Flow: A Siamese Network that Predicts Boundary Motion without Training on Motion—0
DeVOS: Flow-Guided Deformable Transformer for Video Object Segmentation—0
Devon: Deformable Volume Network for Learning Optical Flow—0
An Empirical Study of Visual Features for DNN based Audio-Visual Speech Enhancement in Multi-talker Environments—0
A 3D Morphable Model of Craniofacial Shape and Texture Variation—0
Detection of (Hidden) Emotions from Videos using Muscles Movements and Face Manifold Embedding—0
Detecting Moving Objects Using a Novel Optical-Flow-Based Range-Independent Invariant—0
Detail Preserving Residual Feature Pyramid Modules for Optical Flow—0
Blur Robust Optical Flow using Motion Channel—0
An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes—0
Detail-Enhanced Intra- and Inter-modal Interaction for Audio-Visual Emotion Recognition—0
Detailed Surface Geometry and Albedo Recovery from RGB-D Video Under Natural Illumination—0
Design of Sensor Fusion Driver Assistance System for Active Pedestrian Safety—0
Simulating the gravity in S11 parameters and friis transmission equation—0
Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation—0
An optical flow approach to tracking ship track behavior using GOES-R satellite imagery—0
DepthTransfer: Depth Extraction from Video Using Non-parametric Sampling—0
BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events—0
Depth Map Estimation of Dynamic Scenes Using Prior Depth Information—0
DepthInSpace: Exploitation and Fusion of Multiple Video Frames for Structured-Light Depth Estimation—0
BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation—0
Depth Extraction from Video Using Non-parametric Sampling—0
An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan—0
AdaScan: Adaptive Scan Pooling in Deep Convolutional Neural Networks for Human Action Recognition in Videos—0
4D Gaussian Splatting SLAM—0
DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation—0
BiM-VFI: Bidirectional Motion Field-Guided Frame Interpolation for Video with Non-uniform Motions—0
Dense Scene Flow from Stereo Disparity and Optical Flow—0
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation—0
Dense Optical Flow Prediction from a Static Image—0
Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements—0
Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints—0
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution—0
Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics—0
Dense Monocular Motion Segmentation Using Optical Flow and Pseudo Depth Map: A Zero-Shot Approach—0
Dense Monocular Depth Estimation in Complex Dynamic Scenes—0
Dense Matchers for Dense Tracking—0
Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation—0
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising—0
Dense, Accurate Optical Flow Estimation With Piecewise Parametric Model—0
Denoising-based Turbo Message Passing for Compressed Video Background Subtraction—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