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 351–400 of 2184 papers

TitleStatusHype
BadPart: Unified Black-box Adversarial Patch Attacks against Pixel-wise Regression TasksCode1
FlowDepth: Decoupling Optical Flow for Self-Supervised Monocular Depth Estimation—0
F^2Depth: Self-supervised Indoor Monocular Depth Estimation via Optical Flow Consistency and Feature Map Synthesis—0
OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation—0
AI-Generated Video Detection via Spatio-Temporal Anomaly LearningCode2
Emotion Recognition from the perspective of Activity Recognition—0
DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes—0
CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers—0
DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and MappingCode3
S2DM: Sector-Shaped Diffusion Models for Video Generation—0
TAPTR: Tracking Any Point with Transformers as Detection—0
GaussianFlow: Splatting Gaussian Dynamics for 4D Content Creation—0
Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction—0
GenFlow: Generalizable Recurrent Flow for 6D Pose Refinement of Novel Objects—0
Enhancing Bandwidth Efficiency for Video Motion Transfer Applications using Deep Learning Based Keypoint Prediction—0
Exploring Optical Flow Inclusion into nnU-Net Framework for Surgical Instrument Segmentation—0
NeuFlow: Real-time, High-accuracy Optical Flow Estimation on Robots Using Edge DevicesCode2
Motion-Boundary-Driven Unsupervised Surgical Instrument Segmentation in Low-Quality Optical FlowCode1
Intention-driven Ego-to-Exo Video Generation—0
Flow-Based Visual Stream Compression for Event Cameras—0
LISO: Lidar-only Self-Supervised 3D Object DetectionCode2
Ada-Tracker: Soft Tissue Tracking via Inter-Frame and Adaptive-Template MatchingCode0
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
PIPsUS: Self-Supervised Dense Point Tracking in UltrasoundCode0
Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes—0
HDRFlow: Real-Time HDR Video Reconstruction with Large Motions—0
Motion-Corrected Moving Average: Including Post-Hoc Temporal Information for Improved Video Segmentation—0
Explicit Motion Handling and Interactive Prompting for Video Camouflaged Object Detection—0
Trustworthy Self-Attention: Enabling the Network to Focus Only on the Most Relevant References—0
From Flies to Robots: Inverted Landing in Small Quadcopters with Dynamic Perching—0
Efficient Temporal Extrapolation of Multimodal Large Language Models with Temporal Grounding BridgeCode1
TDE-3: An improved prior for optical flow computation in spiking neural networks—0
Dense Matchers for Dense Tracking—0
What's in the Flow? Exploiting Temporal Motion Cues for Unsupervised Generic Event Boundary Detection—0
Moving Object Proposals with Deep Learned Optical Flow for Video Object Segmentation—0
A Flow-based Credibility Metric for Safety-critical Pedestrian Detection—0
Image-based Deep Learning for the time-dependent prediction of fresh concrete properties—0
A Network for structural dense displacement based on 3D deformable mesh model and optical flow—0
CREMA: Generalizable and Efficient Video-Language Reasoning via Multimodal Modular FusionCode2
Modeling Spatio-temporal Dynamical Systems with Neural Discrete Learning and Levels-of-Experts—0
AnaMoDiff: 2D Analogical Motion Diffusion via Disentangled Denoising—0
Taylor Videos for Action RecognitionCode1
Motion-Aware Video Frame Interpolation—0
Using Motion Cues to Supervise Single-Frame Body Pose and Shape Estimation in Low Data Regimes—0
MUSTAN: Multi-scale Temporal Context as Attention for Robust Video Foreground Segmentation—0
Recurrent Partial Kernel Network for Efficient Optical Flow EstimationCode4
Multimodal Action Quality AssessmentCode1
Exploring the Common Appearance-Boundary Adaptation for Nighttime Optical Flow—0
Motion Guidance: Diffusion-Based Image Editing with Differentiable Motion Estimators—0
CMRNext: Camera to LiDAR Matching in the Wild for Localization and Extrinsic Calibration—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