CVPR 2022 Workshop "Image Matching: Local Features & Beyond"
Channel: Dmytro Mishkin
Date: 2022-11-14

The Fourth Workshop on Image Matching: Local Features and Beyond  held at CVPR 2022 on June 20, 2022. 

0:00 Recap about previous Challenges & workshops
17:39 Keynote "SuperPoint and SuperGlue: Lessons Learned"
58:51 Keynote "Large-scale 3D reconstruction Deployment - Successes, Challenges, Open Problems"
1:30:00 Coffeebreak, no content
1:47:00 Unstructured Object Matching using Co-Salient Region Segmentation
1:58:00 Nerfels: Renderable Neural Codes for Improved Camera Pose Estimation
2:12:21 Feature Query Networks: Neural Surface Description for Camera Pose Refinement 
2:22:12 Learning Co-segmentation by Segment Swapping for Retrieval and Discovery
2:35:00 DA-AE: Disparity-Alleviation Auto-Encoder Towards Categorization of Heritage Images for Aggrandized 3D Reconstruction 
2:41:39 Detecting and Suppressing Marine Snow for Underwater Visual SLAM
2:52:00 A case for using rotation invariant features in state of the art feature matchers
3:04:43 Coffeebreak 2, no content
3:14:56 Image Matching Challenge 2022 Results
3:20:44 1st place talk IMC2022
3:35:19 3rd place talk IMC2022
3:41:08 5th place talk IMC2022
3:49:07 7th place talk IMC2022
3:59:21 Lessons learned & Future work.

** Abstract

Traditional, keypoint-based formulations for image matching are still very competitive on tasks such as Structure from Motion (SfM), despite recent efforts on tackling pose estimation with dense networks. In practice, many state-of-the-art pipelines still rely on methods that stood the test of time, such as SIFT or RANSAC.

In this workshop, we aim to encourage novel strategies for image matching that deviate from and advance traditional formulations, with a focus on large-scale, wide-baseline matching for 3D reconstruction or pose estimation. This can be achieved by applying new technologies to sparse feature matching, or doing away with keypoints and descriptors entirely.

Workshop website: https://image-matching-workshop.github.io