Johan Edstedt

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

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

Jie Zhao, Johan Edstedt, Michael Felsberg, Dong Wang, Huchuan Lu,  Leveraging the Power of Data Augmentation for Transformer-based Tracking, 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 6455-6464, Institute of Electrical and Electronics Engineers (IEEE) (2024)  https://doi.org/10.1109/wacv57701.2024.00634

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

Emil Brissman, Per-Erik Forssén, Johan Edstedt,  Camera Calibration Without Camera Access - A Robust Validation Technique for Extended PnP Methods, , Gade, R., Felsberg, M., Kämäräinen, JK (eds.), Lecture Notes in Computer Science, pp. 34-49 (2023)  https://doi.org/10.1007/978-3-031-31435-3_3