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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
BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events—0
Robust Optical Flow Computation: A Higher-Order Differential Approach—0
Z-upscaling: Optical Flow Guided Frame Interpolation for Isotropic Reconstruction of 3D EM VolumesCode0
Future frame prediction in chest cine MR imaging using the PCA respiratory motion model and dynamically trained recurrent neural networks—0
Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-VisionCode0
DressRecon: Freeform 4D Human Reconstruction from Monocular Video—0
TFS-NeRF: Template-Free NeRF for Semantic 3D Reconstruction of Dynamic SceneCode0
Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming—0
EventHDR: from Event to High-Speed HDR Videos and Beyond—0
Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation—0
Pose-Guided Fine-Grained Sign Language Video Generation—0
Skills Made to Order: Efficient Acquisition of Robot Cooking Skills Guided by Multiple Forms of Internet Data—0
Secrets of Edge-Informed Contrast Maximization for Event-Based Vision—0
Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering—0
Training Datasets Generation for Machine Learning: Application to Vision Based Navigation—0
Multimodal Attention-Enhanced Feature Fusion-based Weekly Supervised Anomaly Violence Detection—0
Continual Learning of Conjugated Visual Representations through Higher-order Motion Flows—0
SHIRE: Enhancing Sample Efficiency using Human Intuition in REinforcement Learning—0
Tracking Virtual Meetings in the Wild: Re-identification in Multi-Participant Virtual Meetings—0
InstantDrag: Improving Interactivity in Drag-based Image Editing—0
Violence detection in videos using deep recurrent and convolutional neural networks—0
Neuromorphic spatiotemporal optical flow: Enabling ultrafast visual perception beyond human capabilities—0
Real-Time Human Action Recognition on Embedded Platforms—0
LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow—0
HMAFlow: Learning More Accurate Optical Flow via Hierarchical Motion Field AlignmentCode0
FlowRetrieval: Flow-Guided Data Retrieval for Few-Shot Imitation LearningCode0
Estimating Dynamic Flow Features in Groups of Tracked Objects—0
Video Diffusion Models are Strong Video Inpainter—0
DemMamba: Alignment-free Raw Video Demoireing with Frequency-assisted Spatio-Temporal Mamba—0
PooDLe: Pooled and dense self-supervised learning from naturalistic videos—0
OPPH: A Vision-Based Operator for Measuring Body Movements for Personal Healthcare—0
CoSEC: A Coaxial Stereo Event Camera Dataset for Autonomous Driving—0
MVInpainter: Learning Multi-View Consistent Inpainting to Bridge 2D and 3D Editing—0
Toward Pedestrian Head Tracking: A Benchmark Dataset and an Information Fusion Network—0
Egocentric Vision Language Planning—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
NOLO: Navigate Only Look Once—0
EMatch: A Unified Framework for Event-based Optical Flow and Stereo Matching—0
SpotFormer: Multi-Scale Spatio-Temporal Transformer for Facial Expression Spotting—0
Event-based Optical Flow on Neuromorphic Processor: ANN vs. SNN Comparison based on Activation SparsificationCode0
Revisit Event Generation Model: Self-Supervised Learning of Event-to-Video Reconstruction with Implicit Neural Representations—0
Occlusion-Aware 3D Motion Interpretation for Abnormal Behavior Detection—0
Real-Time Image Analysis Software Suitable for Resource-Constrained ComputingCode0
Attenuation-Aware Weighted Optical Flow with Medium Transmission Map for Learning-based Visual Odometry in Underwater terrainCode0
Many Perception Tasks are Highly Redundant Functions of their Input Data—0
Fusion Flow-enhanced Graph Pooling Residual Networks for Unmanned Aerial Vehicles Surveillance in Day and Night Dual Visions—0
Improving Unsupervised Video Object Segmentation via Fake Flow Generation—0
Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves—0
Research Experience of an Undergraduate Student in Computer Vision and Robotics—0
Event-based vision on FPGAs -- a survey—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