Mårten Wadenbäck

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

Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck, Andreas Robinson, Cuong Le,  On Learning Deep O(n)-Equivariant Hyperspheres, Proceedings of the 41st International Conference on Machine Learning, Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix (eds.), Proceedings of Machine Learning Research, pp. 35324-35339, PMLR (2024)  https://arxiv.org/abs/2305.15613

Johan Edstedt, Ioannis Athanasiadis, Mårten Wadenbäck, Michael Felsberg,  DKM: Dense Kernelized Feature Matching for Geometry Estimation, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Proceedings:IEEE Conference on Computer Vision and Pattern Recognition, pp. 17765-17775, IEEE Communications Society (2023)  https://doi.org/10.1109/cvpr52729.2023.01704  https://arxiv.org/abs/2202.00667