253 citations · 268 across the 21 of their papers we have counts for
6 papers · 1 filter
Steerers: A framework for rotation equivariant keypoint descriptors
Georg Bökman, Johan Edstedt, Michael Felsberg +1
Image keypoint descriptions that are discriminative and matchable over large changes in viewpoint are vital for 3D reconstruction. However, descriptions output by learned descripto…
Leveraging Cutting Edge Deep Learning Based Image Matching for Reconstructing a Large Scene from Sparse Images
Georg Bökman, Johan Edstedt
We present the top ranked solution for the AISG-SLA Visual Localisation Challenge benchmark (IJCAI 2023), where the task is to estimate relative motion between images taken in sequ…
Leveraging the Power of Data Augmentation for Transformer-based Tracking
Jie Zhao, Johan Edstedt, Michael Felsberg +2
Due to long-distance correlation and powerful pretrained models, transformer-based methods have initiated a breakthrough in visual object tracking performance. Previous works focus…
DeDoDe: Detect, Don't Describe -- Describe, Don't Detect for Local Feature Matching
Johan Edstedt, Georg Bökman, Mårten Wadenbäck +1
Keypoint detection is a pivotal step in 3D reconstruction, whereby sets of (up to) K points are detected in each view of a scene. Crucially, the detected points need to be consiste…
GMSF: Global Matching Scene Flow
Yushan Zhang, Johan Edstedt, Bastian Wandt +3
We tackle the task of scene flow estimation from point clouds. Given a source and a target point cloud, the objective is to estimate a translation from each point in the source poi…
RoMa: Robust Dense Feature Matching
Johan Edstedt, Qiyu Sun, Georg Bökman +2
Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene, and dense methods estimate all such correspondences…