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 851–900 of 2184 papers

TitleStatusHype
Guidance system for Visually Impaired Persons using Deep Learning and Optical flow—0
HSTR-Net: Reference Based Video Super-resolution with Dual Cameras—0
Uncovering wall-shear stress dynamics from neural-network enhanced fluid flow measurements—0
Flow Dynamics Correction for Action Recognition—0
Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection—0
Effortless Cross-Platform Video Codec: A Codebook-Based Method—0
Tabletop Transparent Scene Reconstruction via Epipolar-Guided Optical Flow with Monocular Depth Completion Prior—0
Detecting Moving Objects Using a Novel Optical-Flow-Based Range-Independent Invariant—0
Time-based Mapping of Space Using Visual Motion Invariants—0
TIDE: Temporally Incremental Disparity Estimation via Pattern Flow in Structured Light SystemCode0
A Spatial-Temporal Dual-Mode Mixed Flow Network for Panoramic Video Salient Object Detection—0
Zero-Shot Open-Vocabulary Tracking with Large Pre-Trained Models—0
Improving Discriminative Multi-Modal Learning with Large-Scale Pre-Trained Models—0
UFD-PRiME: Unsupervised Joint Learning of Optical Flow and Stereo Depth through Pixel-Level Rigid Motion Estimation—0
Skin the sheep not only once: Reusing Various Depth Datasets to Drive the Learning of Optical Flow—0
Segmenting the motion components of a video: A long-term unsupervised model—0
Win-Win: Training High-Resolution Vision Transformers from Two Windows—0
Learning Dense Flow Field for Highly-accurate Cross-view Camera Localization—0
Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback—0
IBVC: Interpolation-driven B-frame Video CompressionCode0
Solving Low-Dose CT Reconstruction via GAN with Local Coherence—0
Motion Segmentation from a Moving Monocular Camera—0
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic SegmentationCode0
Conformalized Multimodal Uncertainty Regression and Reasoning—0
Privacy-preserving Early Detection of Epileptic Seizures in VideosCode0
Robust Frame-to-Frame Camera Rotation Estimation in Crowded Scenes—0
GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement—0
Robot Localization and Mapping Final Report -- Sequential Adversarial Learning for Self-Supervised Deep Visual Odometry—0
Joint Self-supervised Depth and Optical Flow Estimation towards Dynamic Objects—0
ATM: Action Temporality Modeling for Video Question Answering—0
Exploring Attention Mechanisms in Integration of Multi-Modal Information for Sign Language Recognition and Translation—0
StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation—0
Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal EchocardiographyCode0
STint: Self-supervised Temporal Interpolation for Geospatial Data—0
Deep Video Codec Control for Vision Models—0
Flexible Techniques for Differentiable Rendering with 3D Gaussians—0
Exploring Human Crowd Patterns and Categorization in Video Footage for Enhanced Security and Surveillance using Computer Vision and Machine Learning—0
Hybrid Models for Facial Emotion Recognition in Children—0
NOVA: NOvel View Augmentation for Neural Composition of Dynamic ObjectsCode0
ResQ: Residual Quantization for Video Perception—0
GPU Accelerated Color Correction and Frame Warping for Real-time Video Stitching—0
Improving Audio-Visual Segmentation with Bidirectional GenerationCode0
FLAME-based Multi-View 3D Face Reconstruction—0
FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow—0
The Devil in the Details: Simple and Effective Optical Flow Synthetic Data Generation—0
The Color Clifford Hardy Signal: Application to Color Edge Detection and Optical Flow—0
ES-MVSNet: Efficient Framework for End-to-end Self-supervised Multi-View Stereo—0
MVFlow: Deep Optical Flow Estimation of Compressed Videos with Motion Vector Prior—0
A Hybrid Approach To Real-Time Multi-Object Tracking—0
SAMFlow: Eliminating Any Fragmentation in Optical Flow with Segment Anything Model—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