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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 551–600 of 741 papers

TitleStatusHype
Scene Motion Decomposition for Learnable Visual Odometry—0
Attribute-Driven Spontaneous Motion in Unpaired Image TranslationCode0
FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow EstimationCode0
Stereo relative pose from line and point feature tripletsCode0
SGANVO: Unsupervised Deep Visual Odometry and Depth Estimation with Stacked Generative Adversarial Networks—0
Event-based Star Tracking via Multiresolution Progressive Hough Transforms—0
PoseConvGRU: A Monocular Approach for Visual Ego-motion Estimation by Learning—0
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics—0
EVDodgeNet: Deep Dynamic Obstacle Dodging with Event CamerasCode0
Iterative Path Reconstruction for Large-Scale Inertial Navigation on Smartphones—0
Motion Estimation of Non-Holonomic Ground Vehicles From a Single Feature Correspondence Measured Over N Views—0
IM-Net for High Resolution Video Frame Interpolation—0
Radar-only ego-motion estimation in difficult settings via graph matching—0
Asynchronous "Events" are Better For Motion Estimation—0
Tightly Coupled 3D Lidar Inertial Odometry and MappingCode0
Visual-Inertial Mapping with Non-Linear Factor RecoveryCode0
Efficient and Robust Registration on the 3D Special Euclidean Group—0
Predicting Future Pedestrian Motion in Video Sequences using Crowd Simulation—0
Probabilistic Regression of Rotations using Quaternion Averaging and a Deep Multi-Headed Network—0
Semantic Nearest Neighbor Fields Monocular Edge Visual-Odometry—0
Quotienting Impertinent Camera Kinematics for 3D Video Stabilization—0
DFineNet: Ego-Motion Estimation and Depth Refinement from Sparse, Noisy Depth Input with RGB Guidance—0
Sparse Representations for Object and Ego-motion Estimation in Dynamic ScenesCode0
Robust Video Background Identification by Dominant Rigid Motion Estimation—0
PEA265: Perceptual Assessment of Video Compression Artifacts—0
Single Image Deblurring and Camera Motion Estimation with Depth Map—0
Sparse Depth Enhanced Direct Thermal-infrared SLAM Beyond the Visible Spectrum—0
ROVO: Robust Omnidirectional Visual Odometry for Wide-baseline Wide-FOV Camera SystemsCode0
DeepLO: Geometry-Aware Deep LiDAR Odometry—0
Single-frame Regularization for Temporally Stable CNNs—0
Beyond Photometric Loss for Self-Supervised Ego-Motion EstimationCode0
Dense Depth Estimation of a Complex Dynamic Scene without Explicit 3D Motion Estimation—0
Towards Segmenting Anything That MovesCode0
A Deep Learning Framework for Assessing Physical Rehabilitation ExercisesCode0
Unsupervised Learning-based Depth Estimation aided Visual SLAM Approach—0
Optimal local estimates of visual motion in a natural environment—0
DeepCalib: a deep learning approach for automatic intrinsic calibration of wide field-of-view camerasCode0
DeepV2D: Video to Depth with Differentiable Structure from MotionCode0
A High-Speed, Real-Time Vision System for Texture Tracking and Thread Counting—0
A New Variational Model for Joint Image Reconstruction and Motion Estimation in Spatiotemporal Imaging—0
Real-time Indoor Scene Reconstruction with RGBD and Inertia Input—0
UnDEMoN 2.0: Improved Depth and Ego Motion Estimation through Deep Image Sampling—0
Real-time high speed motion prediction using fast aperture-robust event-driven visual flow—0
Multi-hierarchical Independent Correlation Filters for Visual TrackingCode0
Topology-Aware Non-Rigid Point Cloud Registration—0
Learned Video Compression—0
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular VideosCode0
Self-Supervised Learning of Depth and Camera Motion from 360° Videos—0
Repetitive Motion Estimation Network: Recover cardiac and respiratory signal from thoracic imaging—0
RGB-D SLAM in Dynamic Environments Using Point Correlations—0
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