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 2101–2150 of 2184 papers

TitleStatusHype
Direct Photometric Alignment by Mesh Deformation—0
Fast Convex Relaxations using Graph Discretizations—0
Fast Edge-Preserving PatchMatch for Large Displacement Optical Flow—0
Fast Event-based Optical Flow Estimation by Triplet Matching—0
Tsanet: Temporal and Scale Alignment for Unsupervised Video Object Segmentation—0
Direct, Dense, and Deformable: Template-Based Non-Rigid 3D Reconstruction From RGB Video—0
Dimensions of Motion: Monocular Prediction through Flow Subspaces—0
Digging Deeper into Egocentric Gaze Prediction—0
Fast Multi-frame Stereo Scene Flow with Motion Segmentation—0
Fast Optical Flow using Dense Inverse Search—0
TSkips: Efficiency Through Explicit Temporal Delay Connections in Spiking Neural Networks—0
Fast Retinomorphic Event Stream for Video Recognition and Reinforcement Learning—0
Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics—0
Fast Semantic Segmentation on Video Using Block Motion-Based Feature Interpolation—0
Fast video object segmentation with Spatio-Temporal GANs—0
Fast Video Salient Object Detection via Spatiotemporal Knowledge Distillation—0
FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution—0
FDFlowNet: Fast Optical Flow Estimation using a Deep Lightweight Network—0
FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting—0
Feasibility of Video-based Sub-meter Localization on Resource-constrained Platforms—0
Feature-Aligned Video Raindrop Removal with Temporal Constraints—0
Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution—0
Feature Flow: In-network Feature Flow Estimation for Video Object Detection—0
DIFT: Dynamic Iterative Field Transforms for Memory Efficient Optical Flow—0
Feature Hallucination for Self-supervised Action Recognition—0
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion—0
Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition—0
Adaptive Semantic-Spatio-Temporal Graph Convolutional Network for Lip Reading—0
Feature-Supervised Action Modality Transfer—0
FEDORA: Flying Event Dataset fOr Reactive behAvior—0
Tracklets Predicting Based Adaptive Graph Tracking—0
Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence—0
DiffPoseNet: Direct Differentiable Camera Pose Estimation—0
Filtered Channel Features for Pedestrian Detection—0
Filtered Feature Channels for Pedestrian Detection—0
Two-dimensional flow field measurement of sediment-laden flow based on ultrasound image velocimetry—0
Finding Correspondences for Optical Flow and Disparity Estimations using a Sub-pixel Convolution-based Encoder-Decoder Network—0
Fine Dense Alignment of Image Bursts through Camera Pose and Depth Estimation—0
KOI: Accelerating Online Imitation Learning via Hybrid Key-state Guidance—0
First order algorithms in variational image processing—0
Differential Scene Flow from Light Field Gradients—0
Plug-in, Trainable Gate for Streamlining Arbitrary Neural Networks—0
Flexible Techniques for Differentiable Rendering with 3D Gaussians—0
FLODCAST: Flow and Depth Forecasting via Multimodal Recurrent Architectures—0
FloVD: Optical Flow Meets Video Diffusion Model for Enhanced Camera-Controlled Video Synthesis—0
Differentiable Forward and Backward Fixed-Point Iteration Layers—0
Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images—0
Flow-Assisted Motion Learning Network for Weakly-Supervised Group Activity Recognition—0
Flow Based Self-supervised Pixel Embedding for Image Segmentation—0
Diffeomorphic Template Registration for Atmospheric Turbulence Mitigation—0
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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