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 651–700 of 2184 papers

TitleStatusHype
BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation—0
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow—0
InstMove: Instance Motion for Object-centric Video SegmentationCode2
PATS: Patch Area Transportation with Subdivision for Local Feature Matching—0
Dynamic Event-based Optical Identification and Communication—0
Scale-aware Two-stage High Dynamic Range Imaging—0
DECOMPL: Decompositional Learning with Attention Pooling for Group Activity Recognition from a Single Volleyball ImageCode0
SemARFlow: Injecting Semantics into Unsupervised Optical Flow Estimation for Autonomous DrivingCode1
Automated crack propagation measurement on asphalt concrete specimens using an optical flow-based deep neural networkCode0
3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective—0
Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow—0
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation—0
Intermediate and Future Frame Prediction of Geostationary Satellite Imagery With Warp and Refine Network—0
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception—0
Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and StereoCode1
FlowFormer++: Masked Cost Volume Autoencoding for Pretraining Optical Flow EstimationCode1
Texture-Based Input Feature Selection for Action Recognition—0
Neural Video Compression with Diverse ContextsCode1
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information—0
RipViz: Finding Rip Currents by Learning Pathline Behavior—0
Analysis of Real-Time Hostile Activitiy Detection from Spatiotemporal Features Using Time Distributed Deep CNNs, RNNs and Attention-Based Mechanisms—0
Multi-scale Motion-Aware Module for Video Action Recognition—0
Deep Learning for Event-based Vision: A Comprehensive Survey and BenchmarksCode1
A Cloud-based Deep Learning Framework for Early Detection of Pushing at Crowded Event EntrancesCode0
One-Shot Face Video Re-enactment using Hybrid Latent Spaces of StyleGAN2—0
AI pipeline for accurate retinal layer segmentation using OCT 3D images—0
Optical flow estimation from event-based cameras and spiking neural networksCode1
Spatiotemporal Deformation Perception for Fisheye Video RectificationCode0
NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM—0
SurgT challenge: Benchmark of Soft-Tissue Trackers for Robotic SurgeryCode1
CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasksCode1
DFlow: Learning to Synthesize Better Optical Flow Datasets via a Differentiable PipelineCode0
Uncertainty-Driven Dense Two-View Structure from Motion—0
Mono-STAR: Mono-camera Scene-level Tracking and ReconstructionCode1
Edge-guided Multi-domain RGB-to-TIR image Translation for Training Vision Tasks with Challenging LabelsCode1
Making Reconstruction-based Method Great Again for Video Anomaly DetectionCode1
Optical Flow Estimation in 360^ Videos: Dataset, Model and Application—0
Flow-guided Semi-supervised Video Object Segmentation—0
Exploiting Optical Flow Guidance for Transformer-Based Video InpaintingCode2
Planar Object Tracking via Weighted Optical FlowCode1
GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning—0
Spatio-Temporal Context Modeling for Road Obstacle Detection—0
Deep Dynamic Scene Deblurring from Optical Flow—0
Optical Flow for Autonomous Driving: Applications, Challenges and Improvements—0
Video Semantic Segmentation with Inter-Frame Feature Fusion and Inner-Frame Feature RefinementCode0
Triple-stream Deep Metric Learning of Great Ape Behavioural Actions—0
Interactive Control over Temporal Consistency while Stylizing Video StreamsCode1
STEPs: Self-Supervised Key Step Extraction and Localization from Unlabeled Procedural VideosCode0
Time-to-Contact Map by Joint Estimation of Up-to-Scale Inverse Depth and Global Motion using a Single Event Camera—0
Explicit Motion Disentangling for Efficient Optical Flow EstimationCode1
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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
4LiteFlowNet-ftAverage End-Point Error5.38—Unverified
5MR-FlowAverage 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
2IRR-PWCAverage End-Point Error1.6—Unverified
3LiteFlowNet-ftAverage End-Point Error1.6—Unverified
4PWC-Net + ft - axXivAverage End-Point Error1.5—Unverified
5FDFlowNet-ftAverage End-Point Error1.5—Unverified
6SelFlowAverage End-Point Error1.5—Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4—Unverified
8LiteFlowNet3Average End-Point Error1.3—Unverified
9LiteFlowNet3-SAverage End-Point Error1.3—Unverified
10MaskFlownet-SAverage 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