Motion Estimation
208 papers with code • 0 benchmarks • 10 datasets
Motion Estimation is used to determine the block-wise or pixel-wise motion vectors between two frames.
Benchmarks
These leaderboards are used to track progress in Motion Estimation
Libraries
Use these libraries to find Motion Estimation models and implementationsDatasets
DDD17
PST900
SegTHOR
EV-IMO
Fisheye
Retinal Microsurgery
Headcam
VBR
Most implemented papers
EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Cameras
To our knowledge, this is the first deep learning -- based solution to the problem of dynamic obstacle avoidance using event cameras on a quadrotor.
Deep Blind Video Super-resolution
csbhr/Deep-Blind-VSR
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ICCV 2021
Existing video super-resolution (SR) algorithms usually assume that the blur kernels in the degradation process are known and do not model the blur kernels in the restoration.
Robust Ego and Object 6-DoF Motion Estimation and Tracking
The problem of tracking self-motion as well as motion of objects in the scene using information from a camera is known as multi-body visual odometry and is a challenging task.
CNN-based Ego-Motion Estimation for Fast MAV Maneuvers
In the field of visual ego-motion estimation for Micro Air Vehicles (MAVs), fast maneuvers stay challenging mainly because of the big visual disparity and motion blur.
Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss
ukcheolshin/thermalsfmlearner-ms
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Based on the proposed module, the photometric consistency loss can provide complementary self-supervision to networks.
Spatiotemporal Registration for Event-based Visual Odometry
The state-of-the-art method of contrast maximisation recovers the motion from a batch of events by maximising the contrast of the image of warped events.
Recurrent Video Deblurring with Blur-Invariant Motion Estimation and Pixel Volumes
codeslake/PVDNet
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To alleviate this problem, we propose two novel approaches to deblur videos by effectively aggregating information from multiple video frames.
Deep Homography Estimation in Dynamic Surgical Scenes for Laparoscopic Camera Motion Extraction
We perform an extensive evaluation of state-of-the-art (SOTA) Deep Neural Networks (DNNs) across multiple compute regimes, finding our method transfers from our camera motion free da Vinci surgery dataset to videos of laparoscopic interventions, outperforming classical homography estimation approaches in both, precision by 41%, and runtime on a CPU by 43%.
Unsupervised Landmark Detection Based Spatiotemporal Motion Estimation for 4D Dynamic Medical Images
yyguo-sjtu/dsd-3d-unsupervised-landmark-detection-based-motion-estimation
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In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ anatomical topology and discard the redundant information that is unnecessary for motion estimation.
End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
KUIS-AI-Tekalp-Research-Group/video-compression
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Conventional video compression (VC) methods are based on motion compensated transform coding, and the steps of motion estimation, mode and quantization parameter selection, and entropy coding are optimized individually due to the combinatorial nature of the end-to-end optimization problem.
