Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares-Reference-Cited by-同舟云学术

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Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares

Published:2023-06 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Muhle Dominik¹,Koestler Lukas¹,Jatavallabhula Krishna Murthy²,Cremers Daniel¹

Affiliation:

1. TU Munich

2. MIT

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10203037/10203050/10203413.pdf?arnumber=10203413

Reference72 articles.

1. Generic methods for optimization-based modeling;domke;Artificial Intelligence and Statistics,2012

2. SuperGlue: Learning Feature Matching With Graph Neural Networks

3. SuperPoint: Self-Supervised Interest Point Detection and Description

4. ORB: An efficient alternative to SIFT or SURF

5. Direct Sparse Odometry

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. L-DYNO: Framework to Learn Consistent Visual Features Using Robot’s Motion;2024 IEEE International Conference on Robotics and Automation (ICRA);2024-05-13

2. DAC: Detector-Agnostic Spatial Covariances for Deep Local Features;2024 International Conference on 3D Vision (3DV);2024-03-18

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