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Motion Estimation

Motion Estimation is used to determine the block-wise or pixel-wise motion vectors between two frames.

Source: MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement

Papers

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Showing 301–350 of 741 papers

TitleStatusHype
LEIA: Latent View-invariant Embeddings for Implicit 3D Articulation—0
Intelligent Knee Sleeves: A Real-time Multimodal Dataset for 3D Lower Body Motion Estimation Using Smart Textile—0
Integrating Dashcam Views Through Inter-Video Mapping—0
Instantaneous Perception of Moving Objects in 3D—0
LiDAR-BEVMTN: Real-Time LiDAR Bird's-Eye View Multi-Task Perception Network for Autonomous Driving—0
Long-Range Motion Trajectories Extraction of Articulated Human Using Mesh Evolution—0
LS-VO: Learning Dense Optical Subspace for Robust Visual Odometry Estimation—0
Measuring breathing induced oesophageal motion and its dosimetric impact—0
Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression—0
Initialization of Monocular Visual Navigation for Autonomous Agents Using Modified Structure from Small Motion—0
Inertial Sensing Meets Artificial Intelligence: Opportunity or Challenge?—0
Deep Reinforcement Learning with Iterative Shift for Visual Tracking—0
Indoor Navigation Assistance for Visually Impaired People via Dynamic SLAM and Panoptic Segmentation with an RGB-D Sensor—0
Deep Parametric Continuous Convolutional Neural Networks—0
Improving HEVC Encoding of Rendered Video Data Using True Motion Information—0
Attacking Motion Estimation with Adversarial Snow—0
Agent Prioritization for Autonomous Navigation—0
Improvements of Motion Estimation and Coding using Neural Networks—0
Improved Motion Plane Adaptive 360-Degree Video Compression Using Affine Motion Models—0
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its effect on Image Correspondence—0
Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and Novel Outliers Detection—0
Intrinsic Temporal Regularization for High-resolution Human Video Synthesis—0
AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression—0
IM-Net for High Resolution Video Frame Interpolation—0
Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?—0
Deep Statistic Shape Model for Myocardium Segmentation—0
Image-Space Gridding for Nonrigid Motion-Corrected MR Image Reconstruction—0
DeepLO: Geometry-Aware Deep LiDAR Odometry—0
Learning-Based Video Coding with Joint Deep Compression and Enhancement—0
Joint cardiac T_1 mapping and cardiac function estimation using a deep manifold framework—0
Joint direct estimation of 3D geometry and 3D motion using spatio temporal gradients—0
Joint Forward-Backward Visual Odometry for Stereo Cameras—0
Joint Large-Scale Motion Estimation and Image Reconstruction—0
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry—0
Image Quality Assessment for Rigid Motion Compensation—0
Joint Optimization for 4D Human-Scene Reconstruction in the Wild—0
Deep Learning for 2D and 3D Rotatable Data: An Overview of Methods—0
Accelerating the Registration of Image Sequences by Spatio-temporal Multilevel Strategies—0
Joint Unsupervised Learning of Optical Flow and Egomotion with Bi-Level Optimization—0
Learning to Extract Motion from Videos in Convolutional Neural Networks—0
Image as an IMU: Estimating Camera Motion from a Single Motion-Blurred Image—0
DELTA: Dense Efficient Long-range 3D Tracking for any video—0
Deep learning based fence segmentation and removal from an image using a video sequence—0
Large Displacement Optical Flow from Nearest Neighbor Fields—0
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling—0
Layered image motion with explicit occlusions, temporal consistency, and depth ordering—0
Layered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask—0
Learned Video Codec with Enriched Reconstruction for CLIC P-frame Coding—0
Learned Video Compression—0
A Fully-Connected Layered Model of Foreground and Background Flow—0
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