Optical Flow Estimation
652 papers with code • 10 benchmarks • 34 datasets
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
Benchmarks
These leaderboards are used to track progress in Optical Flow Estimation
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Libraries
Use these libraries to find Optical Flow Estimation models and implementationsDatasets
KITTI
FlyingThings3D
Vimeo90K
MPI Sintel
Virtual KITTI
MegaDepth
VisDrone
Event-Camera Dataset
N-CARS
MVSEC
Latest papers
Open-DDVM: A Reproduction and Extension of Diffusion Model for Optical Flow Estimation
dqiaole/flowdiffusion_pytorch
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Recently, Google proposes DDVM which for the first time demonstrates that a general diffusion model for image-to-image translation task works impressively well on optical flow estimation task without any specific designs like RAFT.
Dense Optical Tracking: Connecting the Dots
16lemoing/dot
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Code, data, and videos showcasing the capabilities of our approach are available in the project webpage: https://16lemoing. github. io/dot .
SigFormer: Sparse Signal-Guided Transformer for Multi-Modal Human Action Segmentation
Nowadays, the majority of approaches concentrate on the fusion of dense signals (i. e., RGB, optical flow, and depth maps).
StreamFlow: Streamlined Multi-Frame Optical Flow Estimation for Video Sequences
To address this issue, multi-frame optical flow methods leverage adjacent frames to mitigate the local ambiguity.
Flow-Guided Diffusion for Video Inpainting
Video inpainting has been challenged by complex scenarios like large movements and low-light conditions.
Dual-Stream Attention Transformers for Sewer Defect Classification
We propose a dual-stream multi-scale vision transformer (DS-MSHViT) architecture that processes RGB and optical flow inputs for efficient sewer defect classification.
CCMR: High Resolution Optical Flow Estimation via Coarse-to-Fine Context-Guided Motion Reasoning
Attention-based motion aggregation concepts have recently shown their usefulness in optical flow estimation, in particular when it comes to handling occluded regions.
EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision
nvlabs/emernerf
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We present EmerNeRF, a simple yet powerful approach for learning spatial-temporal representations of dynamic driving scenes.
Event-based Background-Oriented Schlieren
uzh-rpg/event-based_vision_resources
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Schlieren imaging is an optical technique to observe the flow of transparent media, such as air or water, without any particle seeding.
Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow
cv-stuttgart/detectiondefenses
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In this paper, we thoroughly examine the currently available detect-and-remove defenses ILP and LGS for a wide selection of state-of-the-art optical flow methods, and illuminate their side effects on the quality and robustness of the final flow predictions.
