Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation-Reference-Cited by-同舟云学术

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Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation

Published:2021-10 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE/CVF International Conference on Computer Vision (ICCV)
language:
Short-container-title:

Author:

Zhang Feihu¹,Woodford Oliver J.¹,Prisacariu Victor¹,Torr Philip H. S.¹

Affiliation:

1. University of Oxford

Funder

Snap

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9709627/9709628/09710315.pdf?arnumber=9710315

Reference73 articles.

1. InterpoNet, a Brain Inspired Neural Network for Optical Flow Dense Interpolation

2. Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes

3. Optical Flow in the Dark

4. MaskFlownet: Asymmetric Feature Matching With Learnable Occlusion Mask

5. Guided Matching Based on Statistical Optical Flow for Fast and Robust Correspondence Analysis

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1. TSwinPose: Enhanced monocular 3D human pose estimation with JointFlow;Expert Systems with Applications;2024-09

2. Dense Continuous-Time Optical Flow From Event Cameras;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-07

3. DeFlow: Decoder of Scene Flow Network in Autonomous Driving;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

4. RAPIDFlow: Recurrent Adaptable Pyramids with Iterative Decoding for Efficient Optical Flow Estimation;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

5. Rethinking RAFT for Efficient Optical Flow;2024 13th Iranian/3rd International Machine Vision and Image Processing Conference (MVIP);2024-03-06

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