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Christian Sormann

3 accepted papers

2024

GMM-IKRS: Gaussian Mixture Models for Interpretable Keypoint Refinement and Scoring

ECCV 2024poster

"The extraction of keypoints in images is at the basis of many computer vision applications, from localization to 3D reconstruction. Keypoints come with a score permitting to rank them according to their quality. While learned keypoints often exhibit better properties than handcrafted ones, their sc…

Cited by 1SourcePDFScholar
2023

S-TREK: Sequential Translation and Rotation Equivariant Keypoints for Local Feature Extraction

ICCV 2023poster

In this work we introduce S-TREK, a novel local feature extractor that combines a deep keypoint detector, which is both translation and rotation equivariant by design, with a lightweight deep descriptor extractor. We train the S-TREK keypoint detector within a framework inspired by reinforcement lea…

Cited by 17PDFScholar
2020

Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems

CVPR 2020poster

It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main difficulties in implementing this in practice are associated with a discrepancy in suitable learning objectives as well as wi…

Cited by 31PDFcodeScholar