Mårten Wadenbäck

Mårten Wadenbäck, Marcus Valtonen Örnhag, Johan Edstedt, Radially Distorted Homographies, Revisited (2025)  https://arxiv.org/abs/2508.21190

Johan Edstedt, Georg Bökman, Mårten Wadenbäck, Michael Felsberg, DaD: Distilled Reinforcement Learning for Diverse Keypoint Detection (2025)  https://arxiv.org/abs/2503.07347

Johan Edstedt, Georg Bökman, Mårten Wadenbäck, Michael Felsberg,  DeDoDe: Detect, Don’t Describe — Describe, Don’t Detect for Local Feature Matching, 2024 International Conference on 3D Vision (3DV), 2024 International Conference on 3D Vision (3DV), Institute of Electrical and Electronics Engineers (IEEE) (2024)  https://doi.org/10.1109/3dv62453.2024.00035  https://arxiv.org/abs/2308.08479

Johan Edstedt, Qiyu Sun, Georg Bökman, Mårten Wadenbäck, Michael Felsberg,  RoMa: Robust Dense Feature Matching, 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19790-19800, Institute of Electrical and Electronics Engineers (IEEE) (2024)  https://doi.org/10.1109/CVPR52733.2024.01871  https://arxiv.org/abs/2305.15404

Pavlo Melnyk, Andreas Robinson, Michael Felsberg, Mårten Wadenbäck,  TetraSphere: A Neural Descriptor for O(3)-Invariant Point Cloud Analysis, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 2024, IEEE Conference on Computer Vision and Pattern Recognition, pp. 5620-5630, IEEE Computer Society (2024)  https://doi.org/10.1109/CVPR52733.2024.00537  https://arxiv.org/abs/2211.14456