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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–500 of 2184 papers

TitleStatusHype
Self-Supervised Linear Motion DeblurringCode1
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional NetworksCode1
Multi-Modal Domain Adaptation for Fine-Grained Action RecognitionCode1
A Large Scale Event-based Detection Dataset for AutomotiveCode1
Temporal Interlacing NetworkCode1
Rethinking Motion Representation: Residual Frames with 3D ConvNets for Better Action RecognitionCode1
A Differentiable Recurrent Surface for Asynchronous Event-Based DataCode1
Deep Video Super-Resolution using HR Optical Flow EstimationCode1
Implementation of the VBM3D Video Denoising Method and Some VariantsCode1
Spotting Macro- and Micro-expression Intervals in Long Video SequencesCode1
GLU-Net: Global-Local Universal Network for Dense Flow and CorrespondencesCode1
Volumetric Correspondence Networks for Optical FlowCode1
Estimating People Flows to Better Count Them in Crowded ScenesCode1
Fast Learning of Temporal Action Proposal via Dense Boundary GeneratorCode1
Volterra Neural Networks (VNNs)Code1
Restoration of Non-rigidly Distorted Underwater Images using a Combination of Compressive Sensing and Local Polynomial Image RepresentationsCode1
UnOS: Unified Unsupervised Optical-Flow and Stereo-Depth Estimation by Watching VideosCode1
What Would You Expect? Anticipating Egocentric Actions with Rolling-Unrolling LSTMs and Modality AttentionCode1
Attention-guided Network for Ghost-free High Dynamic Range ImagingCode1
TDAN: Temporally Deformable Alignment Network for Video Super-ResolutionCode1
DVC: An End-to-end Deep Video Compression FrameworkCode1
Exploiting temporal and depth information for multi-frame face anti-spoofingCode1
Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo VideosCode1
Learning for Video Super-Resolution through HR Optical Flow EstimationCode1
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance SystemsCode1
YouTube-VOS: Sequence-to-Sequence Video Object SegmentationCode1
Future Frame Prediction for Anomaly Detection – A New BaselineCode1
Future Frame Prediction for Anomaly Detection -- A New BaselineCode1
Video Enhancement with Task-Oriented FlowCode1
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost VolumeCode1
Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural NetworksCode1
Hidden Two-Stream Convolutional Networks for Action RecognitionCode1
Optical Flow Estimation using a Spatial Pyramid NetworkCode1
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow EstimationCode1
FlowNet: Learning Optical Flow with Convolutional NetworksCode1
Channel-wise Motion Features for Efficient Motion Segmentation—0
An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan—0
Learning to Track Any Points from Human Motion—0
TLB-VFI: Temporal-Aware Latent Brownian Bridge Diffusion for Video Frame Interpolation—0
EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting—0
Feature Hallucination for Self-supervised Action Recognition—0
Multimodal Fusion SLAM with Fourier AttentionCode0
Inference-Time Gaze Refinement for Micro-Expression Recognition: Enhancing Event-Based Eye Tracking with Motion-Aware Post-ProcessingCode0
Post-Training Quantization for Video Matting—0
UFM: A Simple Path towards Unified Dense Correspondence with Flow—0
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images—0
EV-LayerSegNet: Self-supervised Motion Segmentation using Event Cameras—0
Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments—0
DualX-VSR: Dual Axial SpatialTemporal Transformer for Real-World Video Super-Resolution without Motion Compensation—0
Towards a Generalizable Bimanual Foundation Policy via Flow-based Video Prediction—0
Show:102550
← PrevPage 10 of 44Next →
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