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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 1451–1500 of 2184 papers

TitleStatusHype
Optical Flow in Mostly Rigid Scenes—0
Optical Flow in the Dark—0
Optical Flow Matters: an Empirical Comparative Study on Fusing Monocular Extracted Modalities for Better Steering—0
Optical Flow Method for Measuring Deformation of Soil Specimen Subjected to Torsional Shearing—0
3D wind field profiles from hyperspectral sounders: revisiting optic-flow from a meteorological perspective—0
Optical Flow on Evolving Surfaces with an Application to the Analysis of 4D Microscopy Data—0
Optical Flow on Evolving Surfaces with Space and Time Regularisation—0
Optical Flow Regularization of Implicit Neural Representations for Video Frame Interpolation—0
Optical Flow Representation Alignment Mamba Diffusion Model for Medical Video Generation—0
WorldGen: A Large Scale Generative Simulator—0
Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution—0
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network—0
Optical Flow Techniques for Facial Expression Analysis -- a Practical Evaluation Study—0
Beyond Single Stage Encoder-Decoder Networks: Deep Decoders for Semantic Image Segmentation—0
Optical Flow with Semantic Segmentation and Localized Layers—0
Optimizing deep video representation to match brain activity—0
Optimizing Violence Detection in Video Classification Accuracy through 3D Convolutional Neural Networks—0
Ordered Pooling of Optical Flow Sequences for Action Recognition—0
Oriented Light-Field Windows for Scene Flow—0
Origami: A 803 GOp/s/W Convolutional Network Accelerator—0
Beyond Brightness Constancy: Learning Noise Models for Optical Flow—0
Out of the Room: Generalizing Event-Based Dynamic Motion Segmentation for Complex Scenes—0
PA3D: Pose-Action 3D Machine for Video Recognition—0
BEV-MODNet: Monocular Camera based Bird's Eye View Moving Object Detection for Autonomous Driving—0
Best Vision Technologies Submission to ActivityNet Challenge 2018-Task: Dense-Captioning Events in Videos—0
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products—0
Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation—0
Parallel software implementation of recursive multidimensional digital filters for point-target detection in cluttered infrared scenes—0
Parallel Unbalanced Optimal Transport Regularization for Large Scale Imaging Problems—0
Parametric Object Motion from Blur—0
PaReNeRF: Toward Fast Large-scale Dynamic NeRF with Patch-based Reference—0
Best of Both Worlds: Hybrid SNN-ANN Architecture for Event-based Optical Flow Estimation—0
BATMAN: Bilateral Attention Transformer in Motion-Appearance Neighboring Space for Video Object Segmentation—0
PatchBatch: a Batch Augmented Loss for Optical Flow—0
Patch Match Filter: Efficient Edge-Aware Filtering Meets Randomized Search for Fast Correspondence Field Estimation—0
Patient-Specific Domain Adaptation for Fast Optical Flow Based on Teacher-Student Knowledge Transfer—0
PATS: Patch Area Transportation with Subdivision for Local Feature Matching—0
Batch-Based Activity Recognition from Egocentric Photo-Streams—0
PCA Event-Based Optical Flow for Visual Odometry—0
Balancing Domain Experts for Long-Tailed Camera-Trap Recognition—0
PDWN: Pyramid Deformable Warping Network for Video Interpolation—0
Pedestrian Detection in Low-resolution Imagery by Learning Multi-scale Intrinsic Motion Structures (MIMS)—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—0
Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering—0
Personalizing Human Video Pose Estimation—0
Perturbed State Space Feature Encoders for Optical Flow with Event Cameras—0
Video Action Recognition Using spatio-temporal optical flow video frames—0
Photogeometric Scene Flow for High-Detail Dynamic 3D Reconstruction—0
Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography—0
Back to Event Basics: Self-Supervised Learning of Image Reconstruction for Event Cameras via Photometric Constancy—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