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 1051–1100 of 2184 papers

TitleStatusHype
Data-Driven Optimal Sensor Placement for High-Dimensional System Using Annealing Machine—0
DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild—0
DCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation—0
DCVNet: Dilated Cost Volume Networks for Fast Optical Flow—0
DDCNet: Deep Dilated Convolutional Neural Network for Dense Prediction—0
DDCNet-Multires: Effective Receptive Field Guided Multiresolution CNN for Dense Prediction—0
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
Deep 360^ Optical Flow Estimation Based on Multi-Projection Fusion—0
Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow—0
Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition—0
DeepAVO: Efficient Pose Refining with Feature Distilling for Deep Visual Odometry—0
Deep Crisp Boundaries—0
Deep Crisp Boundaries: From Boundaries to Higher-level Tasks—0
Deep Curiosity Loops in Social Environments—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Deep Dynamic Scene Deblurring from Optical Flow—0
Deep End2End Voxel2Voxel Prediction—0
Deep Flow Collaborative Network for Online Visual Tracking—0
Deep Frame Interpolation—0
DeepHS-HDRVideo: Deep High Speed High Dynamic Range Video Reconstruction—0
Deep Iterative 2D/3D Registration—0
Deep Learning and Hybrid Approaches for Dynamic Scene Analysis, Object Detection and Motion Tracking—0
Deep learning based 2.5D flow field estimation for maximum intensity projections of 4D optical coherence tomography—0
Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery—0
Deep learning based fence segmentation and removal from an image using a video sequence—0
Deep Learning Enabled Semantic Communication Systems for Video Transmission—0
Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos—0
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow—0
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles—0
Deep Motion Boundary Detection—0
Deep Motion Features for Visual Tracking—0
Deep Motion Prior for Weakly-Supervised Temporal Action Localization—0
DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies—0
Deep Neural Networks in Video Human Action Recognition: A Review—0
Deep Optical Flow Estimation Via Multi-Scale Correspondence Structure Learning—0
Deep Predictive Video Compression with Bi-directional Prediction—0
DeepRM: Deep Recurrent Matching for 6D Pose Refinement—0
Deep soccer captioning with transformer: dataset, semantics-related losses, and multi-level evaluation—0
Deep Temporal Interpolation of Radar-based Precipitation—0
Deep-Temporal LSTM for Daily Living Action Recognition—0
Deep Transfer Learning for EEG-based Brain Computer Interface—0
Deep Transport Network for Unsupervised Video Object Segmentation—0
Deep Two-Stream Video Inference for Human Body Pose and Shape Estimation—0
Deep Video Codec Control for Vision Models—0
DeepVIO: Self-supervised Deep Learning of Monocular Visual Inertial Odometry using 3D Geometric Constraints—0
Improve Myocardial Strain Estimation based on Deformable Groupwise Registration with a Locally Low-Rank Dissimilarity Metric—0
Deformation Monitoring of Tunnel using Phase-based Motion Magnification and Optical Flow—0
DeGraF-Flow: Extending DeGraF Features for accurate and efficient sparse-to-dense optical flow estimation—0
DemMamba: Alignment-free Raw Video Demoireing with Frequency-assisted Spatio-Temporal Mamba—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