Georg Bökman

Project Assistant at Signal Processing and Biomedical Engineering

Georg Bökman is a PhD student in the computer vision group. He is participating in the Wallenberg AI, Autonomous Systems and Software Program (WASP).

Source: chalmers.se
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Showing 9 publications

2025

Affine Steerers for Structured Keypoint Description

Georg Bökman, Johan Edstedt, Michael Felsberg et al
Lecture Notes in Computer Science. Vol. 15144, p. 449-468
Paper in proceeding
2024

DeDoDe: Detect, Don't Describe - Describe, Don't Detect for Local Feature Matching

Johan Edstedt, Georg Bökman, Mårten Wadenbäck et al
Proceedings - 2024 International Conference on 3D Vision, 3DV 2024, p. 148-157
Paper in proceeding
2024

Steerers: A Framework for Rotation Equivariant Keypoint Descriptors

Georg Bökman, Johan Edstedt, Michael Felsberg et al
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, p. 4885-4895
Paper in proceeding
2024

DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detector

Johan Edstedt, Georg Bökman, Zhenjun Zhao
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, p. 4245-4253
Paper in proceeding
2023

Investigating how ReLU-networks encode symmetries

Georg Bökman, Fredrik Kahl
Advances in Neural Information Processing Systems. Vol. 36
Paper in proceeding
2023

Rigidity Preserving Image Transformations and Equivariance in Perspective

Lucas Brynte, Georg Bökman, Axel Flinth et al
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 13886 LNCS, p. 59-76
Paper in proceeding
2022

ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds

Georg Bökman, Fredrik Kahl, Axel Flinth
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Vol. 2022-June, p. 10966-10975
Paper in proceeding
2022

A case for using rotation invariant features in state of the art feature matchers

Georg Bökman, Fredrik Kahl
IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. Vol. 2022-June, p. 5106-5115
Paper in proceeding

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