A case for using rotation invariant features in state of the art feature matchers. - Georg Bökman; Fredrik Kahl (CVPR Workshops, 2022)

ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds. - Georg Bökman; Fredrik Kahl; Axel Flinth (CVPR, 2022)

Azimuthal Rotational Equivariance in Spherical Convolutional Neural Networks. - Carl Toft; Georg Bökman; Fredrik Kahl (ICPR, 2022)

In Search of Projectively Equivariant Neural Networks. - Georg Bökman; Axel Flinth; Fredrik Kahl (CoRR, 2022)

Investigating how ReLU-networks encode symmetries. - Georg Bökman; Fredrik Kahl (NeurIPS, 2023)

Rigidity Preserving Image Transformations and Equivariance in Perspective. - Lucas Brynte; Georg Bökman; Axel Flinth; Fredrik Kahl (SCIA (2), 2023)

ICML 2023 Topological Deep Learning Challenge: Design and Results. - Mathilde Papillon; Mustafa Hajij; Audun Myers; Florian Frantzen; Ghada Zamzmi; Helen Jenne; Johan Mathe; Josef Hoppe; Michael T. Schaub; Theodore Papamarkou; Aldo Guzmán-Sáenz; Bastian Rieck; et al. (TAG-ML, 2023)

RoMa: Revisiting Robust Losses for Dense Feature Matching. - Johan Edstedt; Qiyu Sun; Georg Bökman; Mårten Wadenbäck; Michael Felsberg (CoRR, 2023)

ICML 2023 Topological Deep Learning Challenge : Design and Results. - Mathilde Papillon; Mustafa Hajij; Florian Frantzen; Josef Hoppe; Helen Jenne; Johan Mathe; Audun Myers; Theodore Papamarkou; Michael T. Schaub; Ghada Zamzmi; Tolga Birdal; Tamal K. Dey; et al. (CoRR, 2023)

Leveraging Cutting Edge Deep Learning Based Image Matching for Reconstructing a Large Scene from Sparse Images. - Georg Bökman; Johan Edstedt (CoRR, 2023)

In search of projectively equivariant networks. - Georg Bökman; Axel Flinth; Fredrik Kahl (Trans. Mach. Learn. Res., 2023)

DeDoDe: Detect, Don't Describe - Describe, Don't Detect for Local Feature Matching. - Johan Edstedt; Georg Bökman; Mårten Wadenbäck; Michael Felsberg (3DV, 2024)

Steerers: A Framework for Rotation Equivariant Keypoint Descriptors. - Georg Bökman; Johan Edstedt; Michael Felsberg; Fredrik Kahl (CVPR, 2024)

DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detector. - Johan Edstedt; Georg Bökman; Zhenjun Zhao (CVPR Workshops, 2024)

RoMa: Robust Dense Feature Matching. - Johan Edstedt; Qiyu Sun; Georg Bökman; Mårten Wadenbäck; Michael Felsberg (CVPR, 2024)

Affine Steerers for Structured Keypoint Description. - Georg Bökman; Johan Edstedt; Michael Felsberg; Fredrik Kahl (ECCV (86), 2024)

From 2D to 3D: AISG-SLA Visual Localization Challenge. - Jialin Gao; Bill Ong; Darld Lwi; Zhen Hao Ng; Xun Wei Yee; Mun-Thye Mak; Wee Siong Ng; See-Kiong Ng; Hui Ying Teo; Victor Khoo; Georg Bökman; Johan Edstedt; et al. (IJCAI, 2024)

Flopping for FLOPs: Leveraging equivariance for computational efficiency. - Georg Bökman; David Nordström; Fredrik Kahl (CoRR, 2025)

DaD: Distilled Reinforcement Learning for Diverse Keypoint Detection. - Johan Edstedt; Georg Bökman; Mårten Wadenbäck; Michael Felsberg (CoRR, 2025)

A Framework for Reducing the Complexity of Geometric Vision Problems and its Application to Two-View Triangulation with Approximation Bounds. - Felix Rydell; Georg Bökman; Fredrik Kahl; Kathlén Kohn (CoRR, 2025)

Stronger ViTs With Octic Equivariance. - David Nordström; Johan Edstedt; Fredrik Kahl; Georg Bökman (CoRR, 2025)