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 1701–1750 of 2184 papers

TitleStatusHype
SDC - Stacked Dilated Convolution: A Unified Descriptor Network for Dense Matching Tasks—0
Appearance-Based Refinement for Object-Centric Motion Segmentation—0
Video Interpolation using Optical Flow and Laplacian Smoothness—0
A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data—0
Secrets in Computing Optical Flow by Convolutional Networks—0
Secrets of Edge-Informed Contrast Maximization for Event-Based Vision—0
A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data—0
Real-time high speed motion prediction using fast aperture-robust event-driven visual flow—0
SegCodeNet: Color-Coded Segmentation Masks for Activity Detection from Wearable Cameras—0
Video Jigsaw: Unsupervised Learning of Spatiotemporal Context for Video Action Recognition—0
Segment Any Motion in Videos—0
Video Matting via Consistency-Regularized Graph Neural Networks—0
Segmentation-aware Deformable Part Models—0
Segment Graph Based Image Filtering: Fast Structure-Preserving Smoothing—0
Self-Aligned Concave Curve: Illumination Enhancement for Unsupervised Adaptation—0
AnyFlow: Arbitrary Scale Optical Flow with Implicit Neural Representation—0
An Unsupervised Optical Flow Estimation For LiDAR Image Sequences—0
Video Modeling with Correlation Networks—0
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications—0
Selfie Video Stabilization—0
Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions—0
SelfME: Self-Supervised Motion Learning for Micro-Expression Recognition—0
Self-Supervised Approach for Facial Movement Based Optical Flow—0
Self-supervised AutoFlow—0
A Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips—0
A Novel Indoor Positioning System for unprepared firefighting scenarios—0
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach—0
A Novel Hybrid Endoscopic Dataset for Evaluating Machine Learning-based Photometric Image Enhancement Models—0
A Novel Factor Graph-Based Optimization Technique for Stereo Correspondence Estimation—0
Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks—0
3D human pose estimation with adaptive receptive fields and dilated temporal convolutions—0
Self-supervised Learning of Occlusion Aware Flow Guided 3D Geometry Perception with Adaptive Cross Weighted Loss from Monocular Videos—0
Self-supervised Learning with Geometric Constraints in Monocular Video: Connecting Flow, Depth, and Camera—0
Anonymizing Egocentric Videos—0
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings—0
Self-Supervised Motion Magnification by Backpropagating Through Optical Flow—0
Self-supervised Motion Representation via Scattering Local Motion Cues—0
Self-Supervised Object-in-Gripper Segmentation from Robotic Motions—0
Self-supervised Object Motion and Depth Estimation from Video—0
Self-Supervised Real-time Video Stabilization—0
Self-Supervised Regional and Temporal Auxiliary Tasks for Facial Action Unit Recognition—0
Self-Supervised Representation Learning for Visual Anomaly Detection—0
Self-Supervised training for blind multi-frame video denoising—0
Video Person Re-Identification using Learned Clip Similarity Aggregation—0
Self-supervised Video Object Segmentation by Motion Grouping—0
Anomaly detection in non-stationary videos using time-recursive differencing network based prediction—0
Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification—0
Semantically Consistent Video Inpainting with Conditional Diffusion Models—0
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks—0
An Iterated L1 Algorithm for Non-smooth Non-convex Optimization in Computer Vision—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