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 1151–1200 of 2184 papers

TitleStatusHype
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation—0
Delta Distillation for Efficient Video ProcessingCode0
Transframer: Arbitrary Frame Prediction with Generative Models—0
Transferring Dual Stochastic Graph Convolutional Network for Facial Micro-expression Recognition—0
SynWoodScape: Synthetic Surround-view Fisheye Camera Dataset for Autonomous Driving—0
Investigation of Factorized Optical Flows as Mid-Level Representations—0
Exploring Optical-Flow-Guided Motion and Detection-Based Appearance for Temporal Sentence Grounding—0
Audio-visual speech separation based on joint feature representation with cross-modal attention—0
Mixed Reality Depth Contour Occlusion Using Binocular Similarity Matching and Three-dimensional Contour Optimisation—0
Hybrid Tracker with Pixel and Instance for Video Panoptic Segmentation—0
Deep Temporal Interpolation of Radar-based Precipitation—0
Optical flow-based branch segmentation for complex orchard environments—0
Weakly Supervised Instance Segmentation using Motion Information via Optical Flow—0
Efficient Video Segmentation Models with Per-frame Inference—0
Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series—0
FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation—0
Balancing Domain Experts for Long-Tailed Camera-Trap Recognition—0
Deep soccer captioning with transformer: dataset, semantics-related losses, and multi-level evaluation—0
Estimation of Clinical Workload and Patient Activity using Deep Learning and Optical Flow—0
Cross domain knowledge compression in realtime optical flow prediction on ultrasound sequences—0
IFOR: Iterative Flow Minimization for Robotic Object Rearrangement—0
Bi-Directional Semi-Supervised Training of Convolutional Neural Networks for Ultrasound Elastography Displacement Estimation—0
3D-FlowNet: Event-based optical flow estimation with 3D representation—0
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action RecognitionCode0
Splatting-based Synthesis for Video Frame Interpolation—0
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks—0
Design of Sensor Fusion Driver Assistance System for Active Pedestrian Safety—0
Learning Pixel Trajectories with Multiscale Contrastive Random Walks—0
Simulating the gravity in S11 parameters and friis transmission equation—0
KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos—0
ClothFormer: Taming Video Virtual Try-On in All Module—0
Merry Go Round: Rotate a Frame and Fool a DNN—0
Spatial Distribution Patterns of Clownfish in Recirculating Aquaculture Systems—0
Enhanced Frame and Event-Based Simulator and Event-Based Video Interpolation Network—0
HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture—0
hARMS: A Hardware Acceleration Architecture for Real-Time Event-Based Optical Flow—0
Pixel-wise Deep Image Stitching—0
FPPN: Future Pseudo-LiDAR Frame Prediction for Autonomous Driving—0
Controllable Animation of Fluid Elements in Still Images—0
Generalized Closed-form Formulae for Feature-based Subpixel Alignment in Patch-based MatchingCode0
Event Neural NetworksCode0
Dimensions of Motion: Monocular Prediction through Flow Subspaces—0
Low-Fidelity Video Encoder Optimization for Temporal Action Localization—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Two-dimensional flow field measurement of sediment-laden flow based on ultrasound image velocimetry—0
Learning To Segment Dominant Object Motion From Watching Videos—0
Hierarchical interaction network for video object segmentation from referring expressions—0
Learning Generalized Visual Odometry Using Position-Aware Optical Flow and Geometric Bundle Adjustment—0
FlowVOS: Weakly-Supervised Visual Warping for Detail-Preserving and Temporally Consistent Single-Shot Video Object Segmentation—0
MS-nowcasting: Operational Precipitation Nowcasting with Convolutional LSTMs at Microsoft Weather—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