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 1851–1900 of 2184 papers

TitleStatusHype
STATIC : Surface Temporal Affine for TIme Consistency in Video Monocular Depth Estimation—0
STC-Flow: Spatio-temporal Context-aware Optical Flow Estimation—0
Virtual Worlds as Proxy for Multi-Object Tracking Analysis—0
SteadyFlow: Spatially Smooth Optical Flow for Video Stabilization—0
StepNet: Spatial-temporal Part-aware Network for Isolated Sign Language Recognition—0
Vision-based control for landing an aerial vehicle on a marine vessel—0
Stereo 3D Object Trajectory Reconstruction—0
Stereo Any Video: Temporally Consistent Stereo Matching—0
Stereo DSO: Large-Scale Direct Sparse Visual Odometry with Stereo Cameras—0
StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation—0
ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation—0
STint: Self-supervised Temporal Interpolation for Geospatial Data—0
VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal—0
Stochastic Variational Inference with Gradient Linearization—0
A massively parallel multi-level approach to a domain decomposition method for the optical flow estimation with varying illumination—0
Algorithm for the reconstruction of dynamic objects in CT-scanning using optical flow—0
AI pipeline for accurate retinal layer segmentation using OCT 3D images—0
Structure-from-Motion-Aware PatchMatch for Adaptive Optical Flow Estimation—0
Students taught by multimodal teachers are superior action recognizers—0
Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming—0
Subjective Annotation for a Frame Interpolation Benchmark using Artefact Amplification—0
Vision-based localization methods under GPS-denied conditions—0
Visual Descriptor Learning from Monocular Video—0
SUDS: Scalable Urban Dynamic Scenes—0
Superframes, A Temporal Video Segmentation—0
Super Resolution image reconstructs via total variation-based image deconvolution: a majorization-minimization approach—0
Visual Looming from Motion Field and Surface Normals—0
A Hybrid CNN-LSTM model for Video Deepfake Detection by Leveraging Optical Flow Features—0
Visually Guided Sound Source Separation and Localization using Self-Supervised Motion Representations—0
Surgical Gesture Recognition with Optical Flow only—0
SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation—0
A Hybrid Approach To Real-Time Multi-Object Tracking—0
Surveillance Video Parsing with Single Frame Supervision—0
SWTF: Sparse Weighted Temporal Fusion for Drone-Based Activity Recognition—0
SymmNet: A Symmetric Convolutional Neural Network for Occlusion Detection—0
Synergistic Bleeding Region and Point Detection in Surgical Videos—0
Synthetic Data for Deep Learning—0
Visual Rhythm Prediction with Feature-Aligning Network—0
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving—0
Table tennis ball spin estimation with an event camera—0
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior—0
Visuomotor Understanding for Representation Learning of Driving Scenes—0
Taking a Deeper Look at Pedestrians—0
Talking Detection In Collaborative Learning Environments—0
Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow—0
AHMSA-Net: Adaptive Hierarchical Multi-Scale Attention Network for Micro-Expression Recognition—0
TAN: Temporal Affine Network for Real-Time Left Ventricle Anatomical Structure Analysis Based on 2D Ultrasound Videos—0
TAPTR: Tracking Any Point with Transformers as Detection—0
Aggressive Perception-Aware Navigation using Deep Optical Flow Dynamics and PixelMPC—0
Task-Oriented Semantic Communication for Stereo-Vision 3D Object Detection—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
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