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

Showing 16511700 of 2184 papers

TitleStatusHype
Learning Monocular Visual Odometry through Geometry-Aware Curriculum Learning0
On the Importance of Video Action Recognition for Visual Lipreading0
Progressive Sparse Local Attention for Video object detection0
Learning Correspondence from the Cycle-Consistency of TimeCode0
Ontology Based Global and Collective Motion Patterns for Event Classification in Basketball Videos0
A Lightweight Optical Flow CNN -- Revisiting Data Fidelity and RegularizationCode0
Two-Stream Action Recognition-Oriented Video Super-ResolutionCode0
Unsupervised motion saliency map estimation based on optical flow inpainting0
Investigation on Combining 3D Convolution of Image Data and Optical Flow to Generate Temporal Action Proposals0
Demonstration of Vector Flow Imaging using Convolutional Neural Networks0
Video Generation from Single Semantic Label MapCode0
Rolling-Shutter-Aware Differential SfM and Image Rectification0
Characterizing Human Behaviours Using Statistical Motion Descriptor0
Semantic Adversarial Network with Multi-scale Pyramid Attention for Video Classification0
FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of DiffeomorphismsCode0
Single-frame Regularization for Temporally Stable CNNs0
IF-TTN: Information Fused Temporal Transformation Network for Video Action Recognition0
DDFlow: Learning Optical Flow with Unlabeled Data DistillationCode0
Neural Video Compression using Spatio-Temporal Priors0
2D LiDAR Map Prediction via Estimating Motion Flow with GRU0
Motion Equivariant Networks for Event Cameras with the Temporal Normalization Transform0
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation0
Towards Segmenting Anything That MovesCode0
Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression RecognitionCode0
Visual Rhythm Prediction with Feature-Aligning Network0
DeGraF-Flow: Extending DeGraF Features for accurate and efficient sparse-to-dense optical flow estimation0
Deep Video Frame Interpolation using Cyclic Frame GenerationCode0
Never Forget: Balancing Exploration and Exploitation via Learning Optical Flow0
Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion0
Ego-motion Sensor for Unmanned Aerial Vehicles Based on a Single-Board Computer0
Technical Report on Visual Quality Assessment for Frame Interpolation0
Edge SLAM: Edge Points Based Monocular Visual SLAM0
Real-time Joint Object Detection and Semantic Segmentation Network for Automated Driving0
Optical Flow augmented Semantic Segmentation networks for Automated Driving0
DMC-Net: Generating Discriminative Motion Cues for Fast Compressed Video Action Recognition0
Unsupervised Moving Object Detection via Contextual Information SeparationCode0
Learning Independent Object Motion from Unlabelled Stereoscopic Videos0
Flow Based Self-supervised Pixel Embedding for Image Segmentation0
Coupled Recurrent Network (CRN)0
Temporal Hockey Action Recognition via Pose and Optical Flows0
Robustness Meets Deep Learning: An End-to-End Hybrid Pipeline for Unsupervised Learning of Egomotion0
An Optical Flow-Based Approach for Minimally-Divergent Velocimetry Data InterpolationCode0
D3D: Distilled 3D Networks for Video Action RecognitionCode0
Unsupervised Event-based Learning of Optical Flow, Depth, and EgomotionCode0
Learning On-Road Visual Control for Self-Driving Vehicles with Auxiliary Tasks0
Light Weight Color Image Warping with Inter-Channel Information0
Hierarchical Discrete Distribution Decomposition for Match Density EstimationCode0
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry PerceptionCode0
Real-Time Anomaly Detection With HMOF Feature0
DeepV2D: Video to Depth with Differentiable Structure from MotionCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error6.64Unverified
2FastFlowNet-ftAverage End-Point Error4.89Unverified
3UnrolledCostAverage End-Point Error4.69Unverified
4LiteFlowNet-ftAverage End-Point Error4.54Unverified
5FlowNet2Average End-Point Error3.96Unverified
6IRR-PWCAverage End-Point Error3.84Unverified
7SelFlowAverage End-Point Error3.74Unverified
8FDFlowNet-ftAverage End-Point Error3.71Unverified
9ScopeFlowAverage End-Point Error3.59Unverified
10LiteFlowNet2-ftAverage End-Point Error3.48Unverified
#ModelMetricClaimedVerifiedStatus
1SpynetAverage End-Point Error8.36Unverified
2FastFlowNet-ftAverage End-Point Error6.08Unverified
3UnrolledCostAverage End-Point Error5.8Unverified
4MR-FlowAverage End-Point Error5.38Unverified
5LiteFlowNet-ftAverage End-Point Error5.38Unverified
6FDFlowNet-ftAverage End-Point Error5.11Unverified
7LiteFlowNet2-ftAverage End-Point Error4.69Unverified
8IRR-PWCAverage End-Point Error4.58Unverified
9LiteFlowNet3-SAverage End-Point Error4.53Unverified
10ContinualFlow + ftAverage End-Point Error4.52Unverified
#ModelMetricClaimedVerifiedStatus
1PWC-NetF1-all33.7Unverified
2FastFlowNetF1-all33.1Unverified
3FlowNet2F1-all30Unverified
4VCNF1-all25.1Unverified
5HD3F1-all24Unverified
6MaskFlowNetF1-all23.1Unverified
7SCVF1-all19.3Unverified
8RAPIDFlowF1-all17.7Unverified
9CRAFTF1-all17.5Unverified
10RAFTF1-all17.4Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftFl-all11.22Unverified
2UnrolledCostFl-all10.81Unverified
3LiteFlowNet-ftFl-all9.38Unverified
4SelFlowFl-all8.42Unverified
5IRR-PWCFl-all7.65Unverified
6LiteFlowNet2-ftFl-all7.62Unverified
7LiteFlowNet3Fl-all7.34Unverified
8LiteFlowNet3-SFl-all7.22Unverified
9MaskFlownet-SFl-all6.81Unverified
10RAPIDFlowFl-all6.12Unverified
#ModelMetricClaimedVerifiedStatus
1FastFlowNet-ftAverage End-Point Error1.8Unverified
2LiteFlowNet-ftAverage End-Point Error1.6Unverified
3IRR-PWCAverage End-Point Error1.6Unverified
4SelFlowAverage End-Point Error1.5Unverified
5FDFlowNet-ftAverage End-Point Error1.5Unverified
6PWC-Net + ft - axXivAverage End-Point Error1.5Unverified
7LiteFlowNet2-ftAverage End-Point Error1.4Unverified
8LiteFlowNet3-SAverage End-Point Error1.3Unverified
9LiteFlowNet3Average End-Point Error1.3Unverified
10MaskFlownetAverage End-Point Error1.1Unverified
#ModelMetricClaimedVerifiedStatus
1PWCNet1px total82.27Unverified
2SPyNet1px total29.96Unverified
3GMFlow1px total10.36Unverified
4GMA1px total7.07Unverified
5RAFT1px total6.79Unverified
6FlowNet21px total6.71Unverified
7FlowFormer1px total6.51Unverified
8MS-RAFT+1px total5.72Unverified
9RPKNet1px total4.81Unverified
10DPFlow1px total3.44Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error5.21Unverified
2MDFlow-FastAverage End-Point Error4.73Unverified
3UpFlowAverage End-Point Error4.68Unverified
4ARFlow-MVAverage End-Point Error4.49Unverified
5MDFlowAverage End-Point Error4.16Unverified
#ModelMetricClaimedVerifiedStatus
1UFlowAverage End-Point Error6.5Unverified
2MDFlow-FastAverage End-Point Error5.99Unverified
3ARFlow-MVAverage End-Point Error5.67Unverified
4MDFlowAverage End-Point Error5.46Unverified
5UpFlowAverage End-Point Error5.32Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVFl-all11.79Unverified
2MDFlow-FastFl-all11.43Unverified
3UpFlowFl-all9.38Unverified
4MDFlowFl-all8.91Unverified
#ModelMetricClaimedVerifiedStatus
1ARFlow-MVAverage End-Point Error1.5Unverified
2UpFlowAverage End-Point Error1.4Unverified